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Alwakid G, Gouda W, Humayun M, Jhanjhi NZ. Diagnosing Melanomas in Dermoscopy Images Using Deep Learning. Diagnostics (Basel) 2023; 13:diagnostics13101815. [PMID: 37238299 DOI: 10.3390/diagnostics13101815] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 05/04/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
When it comes to skin tumors and cancers, melanoma ranks among the most prevalent and deadly. With the advancement of deep learning and computer vision, it is now possible to quickly and accurately determine whether or not a patient has malignancy. This is significant since a prompt identification greatly decreases the likelihood of a fatal outcome. Artificial intelligence has the potential to improve healthcare in many ways, including melanoma diagnosis. In a nutshell, this research employed an Inception-V3 and InceptionResnet-V2 strategy for melanoma recognition. The feature extraction layers that were previously frozen were fine-tuned after the newly added top layers were trained. This study used data from the HAM10000 dataset, which included an unrepresentative sample of seven different forms of skin cancer. To fix the discrepancy, we utilized data augmentation. The proposed models outperformed the results of the previous investigation with an effectiveness of 0.89 for Inception-V3 and 0.91 for InceptionResnet-V2.
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Affiliation(s)
- Ghadah Alwakid
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakakah 72341, Saudi Arabia
| | - Walaa Gouda
- Department of Electrical Engineering, Shoubra Faculty of Engineering, Benha University, Cairo 11672, Egypt
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 72341, Saudi Arabia
| | - N Z Jhanjhi
- School of Computer Science (SCS), Taylor's University, Subang Jaya 47500, Malaysia
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2
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Araújo ALD, da Silva VM, Kudo MS, de Souza ESC, Saldivia-Siracusa C, Giraldo-Roldán D, Lopes MA, Vargas PA, Khurram SA, Pearson AT, Kowalski LP, de Carvalho ACPDLF, Santos-Silva AR, Moraes MC. Machine learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approach. J Oral Pathol Med 2023; 52:109-118. [PMID: 36599081 DOI: 10.1111/jop.13397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/05/2022] [Accepted: 12/15/2022] [Indexed: 01/06/2023]
Abstract
INTRODUCTION Artificial intelligence models and networks can learn and process dense information in a short time, leading to an efficient, objective, and accurate clinical and histopathological analysis, which can be useful to improve treatment modalities and prognostic outcomes. This paper targets oral pathologists, oral medicinists, and head and neck surgeons to provide them with a theoretical and conceptual foundation of artificial intelligence-based diagnostic approaches, with a special focus on convolutional neural networks, the state-of-the-art in artificial intelligence and deep learning. METHODS The authors conducted a literature review, and the convolutional neural network's conceptual foundations and functionality were illustrated based on a unique interdisciplinary point of view. CONCLUSION The development of artificial intelligence-based models and computer vision methods for pattern recognition in clinical and histopathological image analysis of head and neck cancer has the potential to aid diagnosis and prognostic prediction.
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Affiliation(s)
- Anna Luíza Damaceno Araújo
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil.,Head and Neck Surgery Department and LIM 28, University of São Paulo Medical School, São Paulo, São Paulo, Brazil
| | - Viviane Mariano da Silva
- Institute of Science and Technology, Federal University of São Paulo (ICT-Unifesp), São José dos Campos, São Paulo, Brazil
| | - Maíra Suzuka Kudo
- Institute of Science and Technology, Federal University of São Paulo (ICT-Unifesp), São José dos Campos, São Paulo, Brazil
| | | | - Cristina Saldivia-Siracusa
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil
| | - Daniela Giraldo-Roldán
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil
| | - Marcio Ajudarte Lopes
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil
| | - Pablo Agustin Vargas
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil
| | - Syed Ali Khurram
- Unit of Oral and Maxillofacial Pathology, School of Clinical Dentistry, University of Sheffield, Sheffield, UK
| | - Alexander T Pearson
- Section of Hemathology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois, USA.,University of Chicago Comprehensive Cancer Center, Chicago, Illinois, USA
| | - Luiz Paulo Kowalski
- Head and Neck Surgery Department and LIM 28, University of São Paulo Medical School, São Paulo, São Paulo, Brazil.,Department of Head and Neck Surgery and Otorhinolaryngology, A.C. Camargo Cancer Center, São Paulo, São Paulo, Brazil
| | | | - Alan Roger Santos-Silva
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil
| | - Matheus Cardoso Moraes
- Institute of Science and Technology, Federal University of São Paulo (ICT-Unifesp), São José dos Campos, São Paulo, Brazil
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3
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Diagnostic and Prognostic Deep Learning Applications for Histological Assessment of Cutaneous Melanoma. Cancers (Basel) 2022; 14:cancers14246231. [PMID: 36551716 PMCID: PMC9776963 DOI: 10.3390/cancers14246231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 12/08/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Melanoma is among the most devastating human malignancies. Accurate diagnosis and prognosis are essential to offer optimal treatment. Histopathology is the gold standard for establishing melanoma diagnosis and prognostic features. However, discrepancies often exist between pathologists, and analysis is costly and time-consuming. Deep-learning algorithms are deployed to improve melanoma diagnosis and prognostication from histological images of melanoma. In recent years, the development of these machine-learning tools has accelerated, and machine learning is poised to become a clinical tool to aid melanoma histology. Nevertheless, a review of the advances in machine learning in melanoma histology was lacking. We performed a comprehensive literature search to provide a complete overview of the recent advances in machine learning in the assessment of melanoma based on hematoxylin eosin digital pathology images. In our work, we review 37 recent publications, compare the methods and performance of the reviewed studies, and highlight the variety of promising machine-learning applications in melanoma histology.
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4
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Alwakid G, Gouda W, Humayun M, Sama NU. Melanoma Detection Using Deep Learning-Based Classifications. Healthcare (Basel) 2022; 10:healthcare10122481. [PMID: 36554004 PMCID: PMC9777935 DOI: 10.3390/healthcare10122481] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
One of the most prevalent cancers worldwide is skin cancer, and it is becoming more common as the population ages. As a general rule, the earlier skin cancer can be diagnosed, the better. As a result of the success of deep learning (DL) algorithms in other industries, there has been a substantial increase in automated diagnosis systems in healthcare. This work proposes DL as a method for extracting a lesion zone with precision. First, the image is enhanced using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to improve the image's quality. Then, segmentation is used to segment Regions of Interest (ROI) from the full image. We employed data augmentation to rectify the data disparity. The image is then analyzed with a convolutional neural network (CNN) and a modified version of Resnet-50 to classify skin lesions. This analysis utilized an unequal sample of seven kinds of skin cancer from the HAM10000 dataset. With an accuracy of 0.86, a precision of 0.84, a recall of 0.86, and an F-score of 0.86, the proposed CNN-based Model outperformed the earlier study's results by a significant margin. The study culminates with an improved automated method for diagnosing skin cancer that benefits medical professionals and patients.
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Affiliation(s)
- Ghadah Alwakid
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia
- Correspondence:
| | - Walaa Gouda
- Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia
| | - Najm Us Sama
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, Sarawak, Malaysia
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5
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Curti N, Veronesi G, Dika E, Misciali C, Marcelli E, Giampieri E. Breslow thickness: Geometric interpretation, potential pitfalls, and computer automated estimation. Pathol Res Pract 2022; 238:154117. [PMID: 36126452 DOI: 10.1016/j.prp.2022.154117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/28/2022] [Accepted: 08/31/2022] [Indexed: 11/19/2022]
Abstract
Breslow thickness is one of most important prognostic factor for cutaneous melanoma. To quantify the positions of the melanocytes, the Breslow thickness is defined on a distance metric that is reliable and easy to use in a clinical setting. In this letter, we want to highlight some pitfalls in this distance measurement arising from geometrical issues related to section bending and curling, and their consequences on computer automated estimation.
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Affiliation(s)
- Nico Curti
- eDIMESLab, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Giulia Veronesi
- Dermatology Unit, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Emi Dika
- Dermatology Unit, IRCCS Azienda OspedalieraUniversitaria di Bologna, Sant'Orsola Hospital, Bologna, Italy; Dermatology Unit, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy.
| | - Cosimo Misciali
- Dermatology Unit, IRCCS Azienda OspedalieraUniversitaria di Bologna, Sant'Orsola Hospital, Bologna, Italy
| | - Emanuela Marcelli
- eDIMESLab, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Enrico Giampieri
- eDIMESLab, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
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6
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Nofallah S, Wu W, Liu K, Ghezloo F, Elmore JG, Shapiro LG. Automated analysis of whole slide digital skin biopsy images. Front Artif Intell 2022; 5:1005086. [PMID: 36204597 PMCID: PMC9531680 DOI: 10.3389/frai.2022.1005086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 08/25/2022] [Indexed: 11/23/2022] Open
Abstract
A rapidly increasing rate of melanoma diagnosis has been noted over the past three decades, and nearly 1 in 4 skin biopsies are diagnosed as melanocytic lesions. The gold standard for diagnosis of melanoma is the histopathological examination by a pathologist to analyze biopsy material at both the cellular and structural levels. A pathologist's diagnosis is often subjective and prone to variability, while deep learning image analysis methods may improve and complement current diagnostic and prognostic capabilities. Mitoses are important entities when reviewing skin biopsy cases as their presence carries prognostic information; thus, their precise detection is an important factor for clinical care. In addition, semantic segmentation of clinically important structures in skin biopsies might help the diagnosis pipeline with an accurate classification. We aim to provide prognostic and diagnostic information on skin biopsy images, including the detection of cellular level entities, segmentation of clinically important tissue structures, and other important factors toward the accurate diagnosis of skin biopsy images. This paper is an overview of our work on analysis of digital whole slide skin biopsy images, including mitotic figure (mitosis) detection, semantic segmentation, diagnosis, and analysis of pathologists' viewing patterns, and with new work on melanocyte detection. Deep learning has been applied to our methods for all the detection, segmentation, and diagnosis work. In our studies, deep learning is proven superior to prior approaches to skin biopsy analysis. Our work on analysis of pathologists' viewing patterns is the only such work in the skin biopsy literature. Our work covers the whole spectrum from low-level entities through diagnosis and understanding what pathologists do in performing their diagnoses.
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Affiliation(s)
- Shima Nofallah
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Wenjun Wu
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
| | - Kechun Liu
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States
| | - Fatemeh Ghezloo
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States
| | - Joann G. Elmore
- David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, United States
| | - Linda G. Shapiro
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States
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7
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Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation. Diagnostics (Basel) 2022; 12:diagnostics12071713. [PMID: 35885617 PMCID: PMC9316584 DOI: 10.3390/diagnostics12071713] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/04/2022] [Accepted: 07/12/2022] [Indexed: 11/16/2022] Open
Abstract
Invasive melanoma, a common type of skin cancer, is considered one of the deadliest. Pathologists routinely evaluate melanocytic lesions to determine the amount of atypia, and if the lesion represents an invasive melanoma, its stage. However, due to the complicated nature of these assessments, inter- and intra-observer variability among pathologists in their interpretation are very common. Machine-learning techniques have shown impressive and robust performance on various tasks including healthcare. In this work, we study the potential of including semantic segmentation of clinically important tissue structure in improving the diagnosis of skin biopsy images. Our experimental results show a 6% improvement in F-score when using whole slide images along with epidermal nests and cancerous dermal nest segmentation masks compared to using whole-slide images alone in training and testing the diagnosis pipeline.
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8
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Singh L, Janghel RR, Sahu SP. A boosting-based transfer learning method to address absolute-rarity in skin lesion datasets and prevent weight-drift for melanoma detection. DATA TECHNOLOGIES AND APPLICATIONS 2022. [DOI: 10.1108/dta-10-2021-0296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeAutomated skin lesion analysis plays a vital role in early detection. Having relatively small-sized imbalanced skin lesion datasets impedes learning and dominates research in automated skin lesion analysis. The unavailability of adequate data poses difficulty in developing classification methods due to the skewed class distribution.Design/methodology/approachBoosting-based transfer learning (TL) paradigms like Transfer AdaBoost algorithm can compensate for such a lack of samples by taking advantage of auxiliary data. However, in such methods, beneficial source instances representing the target have a fast and stochastic weight convergence, which results in “weight-drift” that negates transfer. In this paper, a framework is designed utilizing the “Rare-Transfer” (RT), a boosting-based TL algorithm, that prevents “weight-drift” and simultaneously addresses absolute-rarity in skin lesion datasets. RT prevents the weights of source samples from quick convergence. It addresses absolute-rarity using an instance transfer approach incorporating the best-fit set of auxiliary examples, which improves balanced error minimization. It compensates for class unbalance and scarcity of training samples in absolute-rarity simultaneously for inducing balanced error optimization.FindingsPromising results are obtained utilizing the RT compared with state-of-the-art techniques on absolute-rare skin lesion datasets with an accuracy of 92.5%. Wilcoxon signed-rank test examines significant differences amid the proposed RT algorithm and conventional algorithms used in the experiment.Originality/valueExperimentation is performed on absolute-rare four skin lesion datasets, and the effectiveness of RT is assessed based on accuracy, sensitivity, specificity and area under curve. The performance is compared with an existing ensemble and boosting-based TL methods.
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9
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A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Artif Intell Rev 2022. [DOI: 10.1007/s10462-021-10121-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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10
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Xu H, Liu L, Lei X, Mandal M, Lu C. An unsupervised method for histological image segmentation based on tissue cluster level graph cut. Comput Med Imaging Graph 2021; 93:101974. [PMID: 34481236 DOI: 10.1016/j.compmedimag.2021.101974] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 07/11/2021] [Accepted: 08/17/2021] [Indexed: 11/16/2022]
Abstract
While deep learning models have demonstrated outstanding performance in medical image segmentation tasks, histological annotations for training deep learning models are usually challenging to obtain, due to the effort and experience required to carefully delineate tissue structures. In this study, we propose an unsupervised method, termed as tissue cluster level graph cut (TisCut), for segmenting histological images into meaningful compartments (e.g., tumor or non-tumor regions), which aims at assisting histological annotations for downstream supervised models. The TisCut consists of three modules. First, histological tissue objects are clustered based on their spatial proximity and morphological features. The Voronoi diagram is then constructed based on tissue object clustering. In the last module, morphological features computed from the Voronoi diagram are integrated into a region adjacency graph. Image partition is then performed to divide the image into meaningful compartments by using the graph cut algorithm. The TisCut has been evaluated on three histological image sets for necrosis and melanoma detections. Experiments show that the TisCut could provide a comparative performance with U-Net models, which achieves about 70% and 85% Jaccard index coefficients in partitioning brain and skin histological images, respectively. In addition, it shows the potential to be used for generating histological annotations when training masks are difficult to collect for supervised segmentation models.
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Affiliation(s)
- Hongming Xu
- School of Biomedical Engineering at Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Lina Liu
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
| | - Xiujuan Lei
- College of Computer Science, Shaanxi Normal University, Xi'an, Shaanxi 710119, China
| | - Mrinal Mandal
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
| | - Cheng Lu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.
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11
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Rastghalam R, Danyali H, Helfroush MS, Celebi ME, Mokhtari M. Skin Melanoma Detection in Microscopic Images Using HMM-Based Asymmetric Analysis and Expectation Maximization. IEEE J Biomed Health Inform 2021; 25:3486-3497. [PMID: 34003756 DOI: 10.1109/jbhi.2021.3081185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Melanoma is one of the deadliest types of skin cancer with increasing incidence. The most definitive diagnosis method is the histopathological examination of the tissue sample. In this paper, a melanoma detection algorithm is proposed based on decision-level fusion and a Hidden Markov Model (HMM), whose parameters are optimized using Expectation Maximization (EM) and asymmetric analysis. The texture heterogeneity of the samples is determined using asymmetric analysis. A fusion-based HMM classifier trained using EM is introduced. For this purpose, a novel texture feature is extracted based on two local binary patterns, namely local difference pattern (LDP) and statistical histogram features of the microscopic image. Extensive experiments demonstrate that the proposed melanoma detection algorithm yields a total error of less than 0.04%.
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12
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Sayed GI, Soliman MM, Hassanien AE. A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization. Comput Biol Med 2021; 136:104712. [PMID: 34388470 DOI: 10.1016/j.compbiomed.2021.104712] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 07/28/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022]
Abstract
Skin lesion classification plays a crucial role in diagnosing various gene and related local medical cases in the field of dermoscopy. In this paper, a new model for the classification of skin lesions as either normal or melanoma is presented. The proposed melanoma prediction model was evaluated on a large publicly available dataset called ISIC 2020. The main challenge of this dataset is severe class imbalance. This paper proposes an approach to overcome this problem using a random over-sampling method followed by data augmentation. Moreover, a new hybrid version of a convolutional neural network architecture and bald eagle search (BES) optimization is proposed. The BES algorithm is used to find the optimal values of the hyperparameters of a SqueezeNet architecture. The proposed melanoma skin cancer prediction model obtained an overall accuracy of 98.37%, specificity of 96.47%, sensitivity of 100%, f-score of 98.40%, and area under the curve of 99%. The experimental results showed the robustness and efficiency of the proposed model compared with VGG19, GoogleNet, and ResNet50. Additionally, the results showed that the proposed model was very competitive compared with the state of the art.
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Affiliation(s)
| | - Mona M Soliman
- Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt.
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13
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Van Herck Y, Antoranz A, Andhari MD, Milli G, Bechter O, De Smet F, Bosisio FM. Multiplexed Immunohistochemistry and Digital Pathology as the Foundation for Next-Generation Pathology in Melanoma: Methodological Comparison and Future Clinical Applications. Front Oncol 2021; 11:636681. [PMID: 33854972 PMCID: PMC8040928 DOI: 10.3389/fonc.2021.636681] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/12/2021] [Indexed: 12/14/2022] Open
Abstract
The state-of-the-art for melanoma treatment has recently witnessed an enormous revolution, evolving from a chemotherapeutic, "one-drug-for-all" approach, to a tailored molecular- and immunological-based approach with the potential to make personalized therapy a reality. Nevertheless, methods still have to improve a lot before these can reliably characterize all the tumoral features that make each patient unique. While the clinical introduction of next-generation sequencing has made it possible to match mutational profiles to specific targeted therapies, improving response rates to immunotherapy will similarly require a deep understanding of the immune microenvironment and the specific contribution of each component in a patient-specific way. Recent advancements in artificial intelligence and single-cell profiling of resected tumor samples are paving the way for this challenging task. In this review, we provide an overview of the state-of-the-art in artificial intelligence and multiplexed immunohistochemistry in pathology, and how these bear the potential to improve diagnostics and therapy matching in melanoma. A major asset of in-situ single-cell profiling methods is that these preserve the spatial distribution of the cells in the tissue, allowing researchers to not only determine the cellular composition of the tumoral microenvironment, but also study tissue sociology, making inferences about specific cell-cell interactions and visualizing distinctive cellular architectures - all features that have an impact on anti-tumoral response rates. Despite the many advantages, the introduction of these approaches requires the digitization of tissue slides and the development of standardized analysis pipelines which pose substantial challenges that need to be addressed before these can enter clinical routine.
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Affiliation(s)
| | - Asier Antoranz
- Laboratory for Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Madhavi Dipak Andhari
- Laboratory for Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Giorgia Milli
- Laboratory for Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | | | - Frederik De Smet
- Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Francesca Maria Bosisio
- Laboratory for Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
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14
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Wu W, Mehta S, Nofallah S, Knezevich S, May CJ, Chang OH, Elmore JG, Shapiro LG. Scale-Aware Transformers for Diagnosing Melanocytic Lesions. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:163526-163541. [PMID: 35211363 PMCID: PMC8865389 DOI: 10.1109/access.2021.3132958] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Diagnosing melanocytic lesions is one of the most challenging areas of pathology with extensive intra- and inter-observer variability. The gold standard for a diagnosis of invasive melanoma is the examination of histopathological whole slide skin biopsy images by an experienced dermatopathologist. Digitized whole slide images offer novel opportunities for computer programs to improve the diagnostic performance of pathologists. In order to automatically classify such images, representations that reflect the content and context of the input images are needed. In this paper, we introduce a novel self-attention-based network to learn representations from digital whole slide images of melanocytic skin lesions at multiple scales. Our model softly weighs representations from multiple scales, allowing it to discriminate between diagnosis-relevant and -irrelevant information automatically. Our experiments show that our method outperforms five other state-of-the-art whole slide image classification methods by a significant margin. Our method also achieves comparable performance to 187 practicing U.S. pathologists who interpreted the same cases in an independent study. To facilitate relevant research, full training and inference code is made publicly available at https://github.com/meredith-wenjunwu/ScATNet.
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Affiliation(s)
- Wenjun Wu
- Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA 98195, USA
| | - Sachin Mehta
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA
| | - Shima Nofallah
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA
| | | | | | - Oliver H Chang
- Department of Pathology, University of Washington, Seattle, WA 98195, USA
| | - Joann G Elmore
- David Geffen School of Medicine, UCLA, Los Angeles, CA 90024, USA
| | - Linda G Shapiro
- Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA 98195, USA
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
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15
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Xu H, Park S, Hwang TH. Computerized Classification of Prostate Cancer Gleason Scores from Whole Slide Images. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1871-1882. [PMID: 31536012 DOI: 10.1109/tcbb.2019.2941195] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Histological Gleason grading of tumor patterns is one of the most powerful prognostic predictors in prostate cancer. However, manual analysis and grading performed by pathologists are typically subjective and time-consuming. In this paper, we present an automatic technique for Gleason grading of prostate cancer from H&E stained whole slide pathology images using a set of novel completed and statistical local binary pattern (CSLBP) descriptors. First, the technique divides the whole slide image (WSI) into a set of small image tiles, where salient tumor tiles with high nuclei densities are selected for analysis. The CSLBP texture features that encode pixel intensity variations from circularly surrounding neighborhoods are extracted from salient image tiles to characterize different Gleason patterns. Finally, the CSLBP texture features computed from all tiles are integrated and utilized by the multi-class support vector machine (SVM) that assigns patient slides with different Gleason scores such as 6, 7, or ≥ 8. Experiments have been performed on 312 different patient cases selected from the cancer genome atlas (TCGA) and have achieved superior performances over state-of-the-art texture descriptors and baseline methods including deep learning models for prostate cancer Gleason grading.
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Singh L, Janghel RR, Sahu SP. TrCSVM: a novel approach for the classification of melanoma skin cancer using transfer learning. DATA TECHNOLOGIES AND APPLICATIONS 2020. [DOI: 10.1108/dta-06-2020-0126] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the insufficiency of training data that occurred while classifying the lesions as melanoma and non-melanoma.Design/methodology/approachIn this work, a transfer learning (TL) framework Transfer Constituent Support Vector Machine (TrCSVM) is designed for melanoma classification based on feature-based domain adaptation (FBDA) leveraging the support vector machine (SVM) and Transfer AdaBoost (TrAdaBoost). The working of the framework is twofold: at first, SVM is utilized for domain adaptation for learning much transferrable representation between source and target domain. In the first phase, for homogeneous domain adaptation, it augments features by transforming the data from source and target (different but related) domains in a shared-subspace. In the second phase, for heterogeneous domain adaptation, it leverages knowledge by augmenting features from source to target (different and not related) domains to a shared-subspace. Second, TrAdaBoost is utilized to adjust the weights of wrongly classified data in the newly generated source and target datasets.FindingsThe experimental results empirically prove the superiority of TrCSVM than the state-of-the-art TL methods on less-sized datasets with an accuracy of 98.82%.Originality/valueExperiments are conducted on six skin lesion datasets and performance is compared based on accuracy, precision, sensitivity, and specificity. The effectiveness of TrCSVM is evaluated on ten other datasets towards testing its generalizing behavior. Its performance is also compared with two existing TL frameworks (TrResampling, TrAdaBoost) for the classification of melanoma.
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George K, Sankaran P, K PJ. Computer assisted recognition of breast cancer in biopsy images via fusion of nucleus-guided deep convolutional features. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 194:105531. [PMID: 32422473 DOI: 10.1016/j.cmpb.2020.105531] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 04/20/2020] [Accepted: 05/05/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Breast cancer is a commonly detected cancer among women, resulting in a high number of cancer-related mortality. Biopsy performed by pathologists is the final confirmation procedure for breast cancer diagnosis. Computer-aided diagnosis systems can support the pathologist for better diagnosis and also in reducing subjective errors. METHODS In the automation of breast cancer analysis, feature extraction is a challenging task due to the structural diversity of the breast tissue images. Here, we propose a nucleus feature extraction methodology using a convolutional neural network (CNN), 'NucDeep', for automated breast cancer detection. Non-overlapping nuclei patches detected from the images enable the design of a low complexity CNN for feature extraction. A feature fusion approach with support vector machine classifier (FF + SVM) is used to classify breast tumor images based on the extracted CNN features. The feature fusion method transforms the local nuclei features into a compact image-level feature, thus improving the classifier performance. A patch class probability based decision scheme (NucDeep + SVM + PD) for image-level classification is also introduced in this work. RESULTS The proposed framework is evaluated on the publicly available BreaKHis dataset by conducting 5 random trials with 70-30 train-test data split, achieving average image level recognition rate of 96.66 ± 0.77%, 100% specificity and 96.21% sensitivity. CONCLUSION It was found that the proposed NucDeep + FF + SVM model outperforms several recent existing methods and reveals a comparable state of the art performance even with low training complexity. As an effective and inexpensive model, the classification of biopsy images for breast tumor diagnosis introduced in this research will thus help to develop a reliable support tool for pathologists.
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Affiliation(s)
- Kalpana George
- Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala, India.
| | - Praveen Sankaran
- Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala, India.
| | - Paul Joseph K
- Department of Electrical Engineering, National Institute of Technology Calicut, Kerala, India.
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Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion. Comput Biol Med 2020; 124:103954. [DOI: 10.1016/j.compbiomed.2020.103954] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 07/30/2020] [Accepted: 07/30/2020] [Indexed: 01/22/2023]
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Salvi M, Molinaro L, Metovic J, Patrono D, Romagnoli R, Papotti M, Molinari F. Fully automated quantitative assessment of hepatic steatosis in liver transplants. Comput Biol Med 2020; 123:103836. [PMID: 32658781 DOI: 10.1016/j.compbiomed.2020.103836] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/25/2020] [Accepted: 05/25/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND The presence of macro- and microvesicular steatosis is one of the major risk factors for liver transplantation. An accurate assessment of the steatosis percentage is crucial for determining liver graft transplantability, which is currently based on the pathologists' visual evaluations on liver histology specimens. METHOD The aim of this study was to develop and validate a fully automated algorithm, called HEPASS (HEPatic Adaptive Steatosis Segmentation), for both micro- and macro-steatosis detection in digital liver histological images. The proposed method employs a hybrid deep learning framework, combining the accuracy of an adaptive threshold with the semantic segmentation of a deep convolutional neural network. Starting from all white regions, the HEPASS algorithm was able to detect lipid droplets and classify them into micro- or macrosteatosis. RESULTS The proposed method was developed and tested on 385 hematoxylin and eosin (H&E) stained images coming from 77 liver donors. Automated results were compared with manual annotations and nine state-of-the-art techniques designed for steatosis segmentation. In the TEST set, the algorithm was characterized by 97.27% accuracy in steatosis quantification (average error 1.07%, maximum average error 5.62%) and outperformed all the compared methods. CONCLUSIONS To the best of our knowledge, the proposed algorithm is the first fully automated algorithm for the assessment of both micro- and macrosteatosis in H&E stained liver tissue images. Being very fast (average computational time 0.72 s), this algorithm paves the way for automated, quantitative and real-time liver graft assessments.
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Affiliation(s)
- Massimo Salvi
- Politobiomed Lab, Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
| | - Luca Molinaro
- Division of Pathology, AOU Città Della Salute e Della Scienza di Torino, Turin, Italy
| | - Jasna Metovic
- Division of Pathology, Department of Oncology, University of Turin, Turin, Italy
| | - Damiano Patrono
- General Surgery 2U, Liver Transplant Center, AOU Città Della Salute e Della Scienza di Torino, University of Turin, Turin, Italy
| | - Renato Romagnoli
- General Surgery 2U, Liver Transplant Center, AOU Città Della Salute e Della Scienza di Torino, University of Turin, Turin, Italy
| | - Mauro Papotti
- Division of Pathology, Department of Oncology, University of Turin, Turin, Italy
| | - Filippo Molinari
- Politobiomed Lab, Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
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Masoud Abdulhamid IA, Sahiner A, Rahebi J. New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation. BIOMED RESEARCH INTERNATIONAL 2020; 2020:5345923. [PMID: 32351994 PMCID: PMC7178473 DOI: 10.1155/2020/5345923] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 01/26/2020] [Accepted: 02/14/2020] [Indexed: 11/26/2022]
Abstract
In this paper, an algorithm is introduced to solve the global optimization problem for melanoma skin cancer segmentation. The algorithm is based on the smoothing of an auxiliary function that is constructed using a known local minimizer and smoothed by utilising Bezier curves. This function achieves all filled function properties. The proposed optimization method is applied to find the threshold values in melanoma skin cancer images. The proposed algorithm is implemented on PH2, ISBI2016 challenge, and ISBI 2017 challenge datasets for melanoma segmentation. The results show that the proposed algorithm exhibits high accuracy, sensitivity, and specificity compared with other methods.
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Affiliation(s)
| | - Ahmet Sahiner
- Department of Mathematics, Suleyman Demirel University, Isparta, Turkey
| | - Javad Rahebi
- Department of Electrical and Computer Engineering, Altinbas University, Turkey
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Kucharski D, Kleczek P, Jaworek-Korjakowska J, Dyduch G, Gorgon M. Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders. SENSORS 2020; 20:s20061546. [PMID: 32168748 PMCID: PMC7146382 DOI: 10.3390/s20061546] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Revised: 02/28/2020] [Accepted: 03/04/2020] [Indexed: 11/24/2022]
Abstract
In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks having a small number of ground-truth images. We evaluate the proposed deep network architecture for the detection of nests of nevus cells in histopathological images of skin specimens is an important step in dermatopathology. The diagnostic criteria based on the degree of uniformity and symmetry of border irregularities are particularly vital in dermatopathology, in order to distinguish between benign and malignant skin lesions. However, to the best of our knowledge, it is the first described method to segment the nests region. The novelty of our approach is not only the area of research, but, furthermore, we address a problem with a small ground-truth dataset. We propose an effective computer-vision based deep learning tool that can perform the nests segmentation based on an autoencoder architecture with two learning steps. Experimental results verified the effectiveness of the proposed approach and its ability to segment nests areas with Dice similarity coefficient 0.81, sensitivity 0.76, and specificity 0.94, which is a state-of-the-art result.
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Affiliation(s)
- Dariusz Kucharski
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
- Correspondence:
| | - Pawel Kleczek
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
| | - Joanna Jaworek-Korjakowska
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
| | - Grzegorz Dyduch
- Chair of Pathomorphology, Jagiellonian University Medical College, ul. Grzegorzecka 16, 31-531 Krakow, Poland
| | - Marek Gorgon
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
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A High-Accuracy Mathematical Morphology and Multilayer Perceptron-Based Approach for Melanoma Detection. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10031098] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
According to the World Health Organization (WHO), melanoma is the most severe type of skin cancer and is the leading cause of death from skin cancer worldwide. Certain features of melanoma include size, shape, color, or texture changes of a mole. In this work, a novel, robust and efficient method for the detection and classification of melanoma in simple and dermatological images is proposed. It is achieved by using HSV (Hue, Saturation, Value) color space along with mathematical morphology and a Gaussian filter to detect the region of interest and estimate four descriptors: symmetry, edge, color, and size. Although these descriptors have been used for several years, the way they are computed for this proposal is one of the things that enhances the results. Subsequently, a multilayer perceptron is employed to classify between malignant and benign melanoma. Three datasets of simple and dermatological images commonly used in the literature were employed to train and evaluate the performance of the proposed method. According to k-fold cross-validation, the method outperforms three state-of-art works, achieving an accuracy of 98.5% and 98.6%, a sensitivity of 96.68% and 98.05%, and a specificity of 98.15%, and 98.01%, in simple and dermatological images, respectively. The results have proven that its use as an assistive device for the detection of melanoma would improve reliability levels compared to conventional methods.
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Xia R, Boroujeni AM, Shea S, Pan Y, Agrawal R, Yousefi E, Fiel MI, Haseeb MA, Gupta R. Diagnosis of Liver Neoplasms by Computational and Statistical Image Analysis. Gastroenterology Res 2019; 12:288-298. [PMID: 31803308 PMCID: PMC6879028 DOI: 10.14740/gr1210] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 08/12/2019] [Indexed: 12/23/2022] Open
Abstract
Background Distinguishing well-differentiated hepatocellular carcinoma (WD-HCC), hepatocellular adenoma (HA) and non-neoplastic liver tissue (NNLT) solely on morphology is often challenging. The purpose of this study was to evaluate the use of computational image analysis to distinguish WD-HCC, HA and NNLT. Methods Seventy-seven cases comprising of WD-HCC (n = 26), HA (n = 23) and NNLT (n = 28) were retrieved and reviewed. A total of 485 hematoxylin and eosin (H&E) photomicrographs (× 400, 0.09 µm2) of WD-HCC (n = 183), HA (n = 173), NNLT (n = 129) and nine whole-slide scans (three of each diagnosis) were obtained, color deconvoluted and digitally transformed. Quantitative data including nuclear density, nuclear sphericity, nuclear perimeter, and nuclear eccentricity from each image were acquired. The data were analyzed by one-way analysis of variance (ANOVA) with Tukey post hoc test, followed by unsupervised and supervised (Chi-square automatic interaction detection (CHAID)) cluster analysis. Results Unsupervised cluster analysis identified three well defined clusters of WD-HCC, HA and NNLT. Employing the four most discriminating nuclear features, supervised analysis was performed on a training set of 383 images, and validated on the remaining 102 test images. The analysis identified WD-HCC (sensitivity 100%, specificity 98%), HA (sensitivity 71%, specificity 85%) and NNLT (sensitivity 70%, specificity 86%). An analysis of whole-slide images identified WD-HCC with sensitivity and specificity of 100%. Conclusions We have successfully demonstrated that computational image analysis of nuclear features can differentiate WD-HCC from non-malignant liver with high accuracy, and can be used to assist in the histopathological diagnosis of hepatocellular carcinoma.
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Affiliation(s)
- Rong Xia
- Department of Pathology, State University of New York, Downstate Medical Center, Brooklyn, NY 11203, USA
| | - Amir M Boroujeni
- Department of Pathology, State University of New York, Downstate Medical Center, Brooklyn, NY 11203, USA
| | - Stephanie Shea
- Department of Pathology, Mount Sinai Hospital and Icahn School of Medicine, New York, NY 10029, USA
| | - Yongsheng Pan
- Department of Pathology, State University of New York, Downstate Medical Center, Brooklyn, NY 11203, USA
| | - Raag Agrawal
- Department of Pathology, State University of New York, Downstate Medical Center, Brooklyn, NY 11203, USA
| | - Elhem Yousefi
- Department of Pathology, State University of New York, Downstate Medical Center, Brooklyn, NY 11203, USA
| | - M Isabel Fiel
- Department of Pathology, Mount Sinai Hospital and Icahn School of Medicine, New York, NY 10029, USA
| | - M A Haseeb
- Department of Pathology, State University of New York, Downstate Medical Center, Brooklyn, NY 11203, USA
| | - Raavi Gupta
- Department of Pathology, State University of New York, Downstate Medical Center, Brooklyn, NY 11203, USA
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Hosseinzadeh Kassani S, Hosseinzadeh Kassani P. A comparative study of deep learning architectures on melanoma detection. Tissue Cell 2019; 58:76-83. [DOI: 10.1016/j.tice.2019.04.009] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Accepted: 04/19/2019] [Indexed: 11/16/2022]
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A New Approach to Border Irregularity Assessment with Application in Skin Pathology. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9102022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
The border irregularity assessment of tissue structures is an important step in medical diagnostics (e.g., in dermatoscopy, pathology, and cardiology). The diagnostic criteria based on the degree of uniformity and symmetry of border irregularities are particularly vital in dermatopathology, to distinguish between benign and malignant skin lesions. We propose a new method for the segmentation of individual border projections and measuring their morphometry. It is based mainly on analyzing the curvature of the object’s border to identify endpoints of projection bases, and on analyzing object’s skeleton in the graph representation to identify bases of projections and their location along the object’s main axis. The proposed segmentation method has been tested on 25 skin whole slide images of common melanocytic lesions. In total, 825 out of 992 (83%) manually segmented retes (projections of epidermis) were detected correctly and the Jaccard similarity coefficient for the task of detecting retes was 0.798. Experimental results verified the effectiveness of the proposed approach. Our method is particularly well suited for assessing the border irregularity of human epidermis and thus could help develop computer-aided diagnostic algorithms for skin cancer detection.
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Nuclear morphometric analysis in tissue as an objective tool with potential use to improve melanoma staging. Melanoma Res 2019; 29:474-482. [PMID: 30839356 DOI: 10.1097/cmr.0000000000000594] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Alterations in nuclear size and shape are commonly observed in cancers, and its objective evaluation may provide valuable clinical information about the outcome of the disease. Here, we applied the nuclear morphometric analysis in tissues in hematoxylin and eosin-digitized slides of nevi and melanoma, to objectively contribute to the prognostic evaluation of these tumors. To this, we analyzed the nuclear morphometry of 34 melanomas classified according to the TNM stage. Eight cases of melanocytic nevi were used as non-neoplastic tissues to set the non-neoplastic parameters of nuclear morphology. Our samples were set as G1 (control, nevi), G2 (T1T2N0M0), G3 (T3T4N0M0), G4 (T1T2N1M1), and G5 (T3T4N1M1). Image-Pro Plus 6.0 software was used to acquire measurements related to nuclear size (variable: Area) and shape (variables: Aspect, AreaBox, Roundness, and RadiusRatio, which were used to generate the Nuclear Irregularity Index). From these primary variables, a set of secondary variables were generated. All the seven primary and secondary variables related to the nuclear area were different among groups (Pillai's trace P<0.001), whereas Nuclear Irregularity Index, which is the variable related to nuclear shape, did not differ among groups. The secondary variable 'Average Area of Large Nuclei' was able to differ all pairwise comparisons, including thin nonmetastatic from thin metastatic tumors. In conclusion, the objective quantification of nuclear area in hematoxylin and eosin slides may provide objective information about the risk stratification of these tumors and has the potential to be used as an additional method in clinical decision making.
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Khan MA, Akram T, Sharif M, Saba T, Javed K, Lali IU, Tanik UJ, Rehman A. Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion. Microsc Res Tech 2019; 82:741-763. [DOI: 10.1002/jemt.23220] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 11/09/2018] [Accepted: 12/29/2018] [Indexed: 01/22/2023]
Affiliation(s)
- Muhammad Attique Khan
- Department of Computer Science and EngineeringHITEC University Museum Road, Taxila Pakistan
| | - Tallha Akram
- Department of Electrical EngineeringCOMSATS University Islamabad Wah Campus Pakistan
| | - Muhammad Sharif
- Department of Computer ScienceCOMSATS University Islamabad Wah Campus Pakistan
| | - Tanzila Saba
- College of Computer and Information SciencesPrince Sultan University Riyadh SA
| | - Kashif Javed
- Department of RoboticsSMME NUST Islamabad Pakistan
| | - Ikram Ullah Lali
- Department of Computer ScienceUniversity of Gujrat Gujrat Pakistan
| | - Urcun John Tanik
- Computer Science and Information Systems Texas A&M University‐Commerce USA
| | - Amjad Rehman
- Department of Information SystemsAl Yamamah University Riyadh KSA
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