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Seoni S, Shahini A, Meiburger KM, Marzola F, Rotunno G, Acharya UR, Molinari F, Salvi M. All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108200. [PMID: 38677080 DOI: 10.1016/j.cmpb.2024.108200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/20/2024] [Accepted: 04/22/2024] [Indexed: 04/29/2024]
Abstract
BACKGROUND AND OBJECTIVES Artificial intelligence (AI) models trained on multi-centric and multi-device studies can provide more robust insights and research findings compared to single-center studies. However, variability in acquisition protocols and equipment can introduce inconsistencies that hamper the effective pooling of multi-source datasets. This systematic review evaluates strategies for image harmonization, which standardizes appearances to enable reliable AI analysis of multi-source medical imaging. METHODS A literature search using PRISMA guidelines was conducted to identify relevant papers published between 2013 and 2023 analyzing multi-centric and multi-device medical imaging studies that utilized image harmonization approaches. RESULTS Common image harmonization techniques included grayscale normalization (improving classification accuracy by up to 24.42 %), resampling (increasing the percentage of robust radiomics features from 59.5 % to 89.25 %), and color normalization (enhancing AUC by up to 0.25 in external test sets). Initially, mathematical and statistical methods dominated, but machine and deep learning adoption has risen recently. Color imaging modalities like digital pathology and dermatology have remained prominent application areas, though harmonization efforts have expanded to diverse fields including radiology, nuclear medicine, and ultrasound imaging. In all the modalities covered by this review, image harmonization improved AI performance, with increasing of up to 24.42 % in classification accuracy and 47 % in segmentation Dice scores. CONCLUSIONS Continued progress in image harmonization represents a promising strategy for advancing healthcare by enabling large-scale, reliable analysis of integrated multi-source datasets using AI. Standardizing imaging data across clinical settings can help realize personalized, evidence-based care supported by data-driven technologies while mitigating biases associated with specific populations or acquisition protocols.
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Affiliation(s)
- Silvia Seoni
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Alen Shahini
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Kristen M Meiburger
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Francesco Marzola
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Giulia Rotunno
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia; Centre for Health Research, University of Southern Queensland, Australia
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Massimo Salvi
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
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Verboom SD, Caballo M, Peters J, Gommers J, van den Oever D, Broeders MJM, Teuwen J, Sechopoulos I. Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors. J Med Imaging (Bellingham) 2024; 11:014001. [PMID: 38162417 PMCID: PMC10753125 DOI: 10.1117/1.jmi.11.1.014001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/25/2023] [Accepted: 12/11/2023] [Indexed: 01/03/2024] Open
Abstract
Purpose We developed a segmentation method suited for both raw (for processing) and processed (for presentation) digital mammograms (DMs) that is designed to generalize across images acquired with systems from different vendors and across the two standard screening views. Approach A U-Net was trained to segment mammograms into background, breast, and pectoral muscle. Eight different datasets, including two previously published public sets and six sets of DMs from as many different vendors, were used, totaling 322 screen film mammograms (SFMs) and 4251 DMs (2821 raw/processed pairs and 1430 only processed) from 1077 different women. Three experiments were done: first training on all SFM and processed images, second also including all raw images in training, and finally testing vendor generalization by leaving one dataset out at a time. Results The model trained on SFM and processed mammograms achieved a good overall performance regardless of projection and vendor, with a mean (±std. dev.) dice score of 0.96 ± 0.06 for all datasets combined. When raw images were included in training, the mean (±std. dev.) dice score for the raw images was 0.95 ± 0.05 and for the processed images was 0.96 ± 0.04 . Testing on a dataset with processed DMs from a vendor that was excluded from training resulted in a difference in mean dice varying between - 0.23 to + 0.02 from that of the fully trained model. Conclusions The proposed segmentation method yields accurate overall segmentation results for both raw and processed mammograms independent of view and vendor. The code and model weights are made available.
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Affiliation(s)
- Sarah D. Verboom
- Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands
| | - Marco Caballo
- Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands
| | - Jim Peters
- Radboud University Medical Center, Department for Health Evidence, Nijmegen, The Netherlands
| | - Jessie Gommers
- Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands
| | - Daan van den Oever
- Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands
| | - Mireille J. M. Broeders
- Radboud University Medical Center, Department for Health Evidence, Nijmegen, The Netherlands
- Dutch Expert Centre for Screening (LRCB), Nijmegen, The Netherlands
| | - Jonas Teuwen
- Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands
- Netherlands Cancer Institute, Department of Radiation Oncology, Amsterdam, The Netherlands
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, New York, United States
| | - Ioannis Sechopoulos
- Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands
- Dutch Expert Centre for Screening (LRCB), Nijmegen, The Netherlands
- University of Twente, Multi-Modality Medical Imaging, Enschede, The Netherlands
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Hao D, Li H, Zhang Y, Zhang Q. MUE-CoT: multi-scale uncertainty entropy-aware co-training framework for left atrial segmentation. Phys Med Biol 2023; 68:215008. [PMID: 37567214 DOI: 10.1088/1361-6560/acef8e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/11/2023] [Indexed: 08/13/2023]
Abstract
Objective.Accurate left atrial segmentation is the basis of the recognition and clinical analysis of atrial fibrillation. Supervised learning has achieved some competitive segmentation results, but the high annotation cost often limits its performance. Semi-supervised learning is implemented from limited labeled data and a large amount of unlabeled data and shows good potential in solving practical medical problems.Approach. In this study, we proposed a collaborative training framework for multi-scale uncertain entropy perception (MUE-CoT) and achieved efficient left atrial segmentation from a small amount of labeled data. Based on the pyramid feature network, learning is implemented from unlabeled data by minimizing the pyramid prediction difference. In addition, novel loss constraints are proposed for co-training in the study. The diversity loss is defined as a soft constraint so as to accelerate the convergence and a novel multi-scale uncertainty entropy calculation method and a consistency regularization term are proposed to measure the consistency between prediction results. The quality of pseudo-labels cannot be guaranteed in the pre-training period, so a confidence-dependent empirical Gaussian function is proposed to weight the pseudo-supervised loss.Main results.The experimental results of a publicly available dataset and an in-house clinical dataset proved that our method outperformed existing semi-supervised methods. For the two datasets with a labeled ratio of 5%, the Dice similarity coefficient scores were 84.94% ± 4.31 and 81.24% ± 2.4, the HD95values were 4.63 mm ± 2.13 and 3.94 mm ± 2.72, and the Jaccard similarity coefficient scores were 74.00% ± 6.20 and 68.49% ± 3.39, respectively.Significance.The proposed model effectively addresses the challenges of limited data samples and high costs associated with manual annotation in the medical field, leading to enhanced segmentation accuracy.
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Affiliation(s)
- Dechen Hao
- School of Software, North University of China, Taiyuan Shanxi, People's Republic of China
| | - Hualing Li
- School of Software, North University of China, Taiyuan Shanxi, People's Republic of China
| | - Yonglai Zhang
- School of Software, North University of China, Taiyuan Shanxi, People's Republic of China
| | - Qi Zhang
- Department of Cardiology, The Second Hospital of Shanxi Medical University, Taiyuan Shanxi, People's Republic of China
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4
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Lin X, Wu S, Li L, Ouyang R, Ma J, Yi C, Tang Y. Automatic mammographic breast density classification in Chinese women: clinical validation of a deep learning model. Acta Radiol 2023; 64:1823-1830. [PMID: 36683330 DOI: 10.1177/02841851231152097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
BACKGROUND High breast density is a strong risk factor for breast cancer. As such, high consistency and accuracy in breast density assessment is necessary. PURPOSE To validate our proposed deep learning (DL) model and explore its impact on radiologists on density assessments. MATERIAL AND METHODS A total of 3732 mammographic cases were collected as a validated set: 1686 cases before the implementation of the DL model and 2046 cases after the DL model. Five radiologists were divided into two groups (junior and senior groups) to assess all mammograms using either two- or four-category evaluation. Linear-weighted kappa (K) and intraclass correlation coefficient (ICC) statistics were used to analyze the consistency between radiologists before and after implementation of the DL model. RESULTS The accuracy and clinical acceptance of the DL model for the junior group were 96.3% and 96.8% for two-category evaluation, and 85.6% and 89.6% for four-category evaluation, respectively. For the senior group, the accuracy and clinical acceptance were 95.5% and 98.0% for two-category evaluation, and 84.3% and 95.3% for four-category evaluation, respectively. The consistency within the junior group, the senior group, and among all radiologists improved with the help of the DL model. For two-category, their K and ICC values improved to 0.81, 0.81, and 0.80 from 0.73, 0.75, and 0.76. And for four-category, their K and ICC values improved to 0.81, 0.82, and 0.82 from 0.73, 0.79, and 0.78, respectively. CONCLUSION The DL model showed high accuracy and clinical acceptance in breast density categories. It is helpful to improve radiologists' consistency.
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Affiliation(s)
- Xiaohui Lin
- Department of Radiology, 12387Shenzhen People's Hospital, the Second Clinical Medical College of Jinan University, Shenzhen, PR China
| | - Shibin Wu
- 537598Ping-An Technology, Shenzhen China, Shenzhen, PR China
| | - Lin Li
- Department of Radiology, 12387Shenzhen People's Hospital, the Second Clinical Medical College of Jinan University, Shenzhen, PR China
| | - Rushan Ouyang
- Department of Radiology, 12387Shenzhen People's Hospital, the Second Clinical Medical College of Jinan University, Shenzhen, PR China
| | - Jie Ma
- Department of Radiology, 12387Shenzhen People's Hospital, the Second Clinical Medical College of Jinan University, Shenzhen, PR China
| | - Chunyan Yi
- Department of Radiology, 12387Shenzhen People's Hospital, the Second Clinical Medical College of Jinan University, Shenzhen, PR China
| | - Yuxing Tang
- 537598Ping-An Technology, Shenzhen China, Shenzhen, PR China
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Sheng C, Wang L, Huang Z, Wang T, Guo Y, Hou W, Xu L, Wang J, Yan X. Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs. JOURNAL OF SYSTEMS SCIENCE AND COMPLEXITY 2022; 36:257-272. [PMID: 36258771 PMCID: PMC9561331 DOI: 10.1007/s11424-022-2057-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/23/2022] [Indexed: 05/28/2023]
Abstract
Panoramic radiographs can assist dentist to quickly evaluate patients' overall oral health status. The accurate detection and localization of tooth tissue on panoramic radiographs is the first step to identify pathology, and also plays a key role in an automatic diagnosis system. However, the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist, while the interpretation of panoramic radiographs might lead misdiagnosis. Therefore, it is of great significance to use artificial intelligence to segment teeth on panoramic radiographs. In this study, SWin-Unet, the transformer-based Ushaped encoder-decoder architecture with skip-connections, is introduced to perform panoramic radiograph segmentation. To well evaluate the tooth segmentation performance of SWin-Unet, the PLAGH-BH dataset is introduced for the research purpose. The performance is evaluated by F1 score, mean intersection and Union (IoU) and Acc, Compared with U-Net, Link-Net and FPN baselines, SWin-Unet performs much better in PLAGH-BH tooth segmentation dataset. These results indicate that SWin-Unet is more feasible on panoramic radiograph segmentation, and is valuable for the potential clinical application.
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Affiliation(s)
- Chen Sheng
- Medical School of Chinese PLA, Beijing, 100853 China
- Department of Stomatology, the first Medical Centre, Chinese PLA General Hospital, Beijing, 100853 China
| | - Lin Wang
- Medical School of Chinese PLA, Beijing, 100853 China
- Department of Stomatology, the first Medical Centre, Chinese PLA General Hospital, Beijing, 100853 China
- Beihang University, Beijing, 100191 China
| | - Zhenhuan Huang
- Medical School of Chinese PLA, Beijing, 100853 China
- Department of Stomatology, the first Medical Centre, Chinese PLA General Hospital, Beijing, 100853 China
- Beihang University, Beijing, 100191 China
| | - Tian Wang
- Medical School of Chinese PLA, Beijing, 100853 China
- Department of Stomatology, the first Medical Centre, Chinese PLA General Hospital, Beijing, 100853 China
- Beihang University, Beijing, 100191 China
| | - Yalin Guo
- Medical School of Chinese PLA, Beijing, 100853 China
- Department of Stomatology, the first Medical Centre, Chinese PLA General Hospital, Beijing, 100853 China
- Beihang University, Beijing, 100191 China
| | - Wenjie Hou
- Medical School of Chinese PLA, Beijing, 100853 China
- Department of Stomatology, the first Medical Centre, Chinese PLA General Hospital, Beijing, 100853 China
- Beihang University, Beijing, 100191 China
| | - Laiqing Xu
- Medical School of Chinese PLA, Beijing, 100853 China
- Department of Stomatology, the first Medical Centre, Chinese PLA General Hospital, Beijing, 100853 China
- Beihang University, Beijing, 100191 China
| | - Jiazhu Wang
- Medical School of Chinese PLA, Beijing, 100853 China
- Department of Stomatology, the first Medical Centre, Chinese PLA General Hospital, Beijing, 100853 China
- Beihang University, Beijing, 100191 China
| | - Xue Yan
- Medical School of Chinese PLA, Beijing, 100853 China
- Department of Stomatology, the first Medical Centre, Chinese PLA General Hospital, Beijing, 100853 China
- Beihang University, Beijing, 100191 China
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Larroza A, Pérez-Benito FJ, Perez-Cortes JC, Román M, Pollán M, Pérez-Gómez B, Salas-Trejo D, Casals M, Llobet R. Breast Dense Tissue Segmentation with Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach. Diagnostics (Basel) 2022; 12:diagnostics12081822. [PMID: 36010173 PMCID: PMC9406546 DOI: 10.3390/diagnostics12081822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 07/18/2022] [Accepted: 07/25/2022] [Indexed: 11/30/2022] Open
Abstract
Breast density assessed from digital mammograms is a known biomarker related to a higher risk of developing breast cancer. Supervised learning algorithms have been implemented to determine this. However, the performance of these algorithms depends on the quality of the ground-truth information, which expert readers usually provide. These expert labels are noisy approximations to the ground truth, as there is both intra- and inter-observer variability among them. Thus, it is crucial to provide a reliable method to measure breast density from mammograms. This paper presents a fully automated method based on deep learning to estimate breast density, including breast detection, pectoral muscle exclusion, and dense tissue segmentation. We propose a novel confusion matrix (CM)—YNet model for the segmentation step. This architecture includes networks to model each radiologist’s noisy label and gives the estimated ground-truth segmentation as well as two parameters that allow interaction with a threshold-based labeling tool. A multi-center study involving 1785 women whose “for presentation” mammograms were obtained from 11 different medical facilities was performed. A total of 2496 mammograms were used as the training corpus, and 844 formed the testing corpus. Additionally, we included a totally independent dataset from a different center, composed of 381 women with one image per patient. Each mammogram was labeled independently by two expert radiologists using a threshold-based tool. The implemented CM-Ynet model achieved the highest DICE score averaged over both test datasets (0.82±0.14) when compared to the closest dense-tissue segmentation assessment from both radiologists. The level of concordance between the two radiologists showed a DICE score of 0.76±0.17. An automatic breast density estimator based on deep learning exhibited higher performance when compared with two experienced radiologists. This suggests that modeling each radiologist’s label allows for better estimation of the unknown ground-truth segmentation. The advantage of the proposed model is that it also provides the threshold parameters that enable user interaction with a threshold-based tool.
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Affiliation(s)
- Andrés Larroza
- Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, 46022 València, Spain; (F.J.P.-B.); (J.-C.P.-C.); (R.L.)
- Correspondence:
| | - Francisco Javier Pérez-Benito
- Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, 46022 València, Spain; (F.J.P.-B.); (J.-C.P.-C.); (R.L.)
| | - Juan-Carlos Perez-Cortes
- Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, 46022 València, Spain; (F.J.P.-B.); (J.-C.P.-C.); (R.L.)
| | - Marta Román
- Department of Epidemiology and Evaluation, IMIM (Hospital del Mar Medical Research Institute), Passeig Marítim 25–29, 08003 Barcelona, Spain;
| | - Marina Pollán
- National Center for Epidemiology, Carlos III Institute of Health, Monforte de Lemos, 5, 28029 Madrid, Spain; (M.P.); (B.P.-G.)
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública—CIBERESP), Carlos III Institute of Health, Monforte de Lemos, 5, 28029 Madrid, Spain
| | - Beatriz Pérez-Gómez
- National Center for Epidemiology, Carlos III Institute of Health, Monforte de Lemos, 5, 28029 Madrid, Spain; (M.P.); (B.P.-G.)
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública—CIBERESP), Carlos III Institute of Health, Monforte de Lemos, 5, 28029 Madrid, Spain
| | - Dolores Salas-Trejo
- Valencian Breast Cancer Screening Program, General Directorate of Public Health, 46022 València, Spain; (D.S.-T.); (M.C.)
- Centro Superior de Investigación en Salud Pública, CSISP, FISABIO, 46020 València, Spain
| | - María Casals
- Valencian Breast Cancer Screening Program, General Directorate of Public Health, 46022 València, Spain; (D.S.-T.); (M.C.)
- Centro Superior de Investigación en Salud Pública, CSISP, FISABIO, 46020 València, Spain
| | - Rafael Llobet
- Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, 46022 València, Spain; (F.J.P.-B.); (J.-C.P.-C.); (R.L.)
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Tiryaki V, Kaplanoğlu V. Deep Learning-Based Multi-Label Tissue Segmentation and Density Assessment from Mammograms. Ing Rech Biomed 2022. [DOI: 10.1016/j.irbm.2022.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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8
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Gastounioti A, Desai S, Ahluwalia VS, Conant EF, Kontos D. Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review. Breast Cancer Res 2022; 24:14. [PMID: 35184757 PMCID: PMC8859891 DOI: 10.1186/s13058-022-01509-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 02/08/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening. MAIN BODY This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman's inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field. CONCLUSIONS We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.
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Affiliation(s)
- Aimilia Gastounioti
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.,Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Shyam Desai
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Vinayak S Ahluwalia
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.,Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Emily F Conant
- Department of Radiology, Hospital of the University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Despina Kontos
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.
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9
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Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06804-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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10
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Ryan F, Román KLL, Gerbolés BZ, Rebescher KM, Txurio MS, Ugarte RC, González MJG, Oliver IM. Unsupervised domain adaptation for the segmentation of breast tissue in mammography images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106368. [PMID: 34537490 DOI: 10.1016/j.cmpb.2021.106368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 08/17/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Breast density refers to the proportion of glandular and fatty tissue in the breast and is recognized as a useful factor assessing breast cancer risk. Moreover, the segmentation of the high-density glandular tissue from mammograms can assist medical professionals visualizing and localizing areas that may require additional attention. Developing robust methods to segment breast tissues is challenging due to the variations in mammographic acquisition systems and protocols. Deep learning methods are effective in medical image segmentation but they often require large quantities of labelled data. Unsupervised domain adaptation is an area of research that employs unlabelled data to improve model performance on variations of samples derived from different sources. METHODS First, a U-Net architecture was used to perform segmentation of the fatty and glandular tissues with labelled data from a single acquisition device. Then, adversarial-based unsupervised domain adaptation methods were used to incorporate single unlabelled target domains, consisting of images from a different machine, into the training. Finally, the domain adaptation model was extended to include multiple unlabelled target domains by combining a reconstruction task with adversarial training. RESULTS The adversarial training was found to improve the generalization of the initial model on new domain data, demonstrating clearly improved segmentation of the breast tissues. For training with multiple unlabelled domains, combining a reconstruction task with adversarial training improved the stability of the training and yielded adequate segmentation results across all domains with a single model. CONCLUSIONS Results demonstrated the potential for adversarial-based domain adaptation with U-Net architectures for segmentation of breast tissue in mammograms coming from several devices and demonstrated that domain-adapted models could achieve a similar agreement with manual segmentations. It has also been found that combining adversarial and reconstruction-based methods can provide a simple and effective solution for training with multiple unlabelled target domains.
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Catala ODT, Igual IS, Perez-Benito FJ, Escriva DM, Castello VO, Llobet R, Perez-Cortes JC. Bias Analysis on Public X-Ray Image Datasets of Pneumonia and COVID-19 Patients. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:42370-42383. [PMID: 34812384 PMCID: PMC8545228 DOI: 10.1109/access.2021.3065456] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 03/07/2021] [Indexed: 05/03/2023]
Abstract
Chest X-ray images are useful for early COVID-19 diagnosis with the advantage that X-ray devices are already available in health centers and images are obtained immediately. Some datasets containing X-ray images with cases (pneumonia or COVID-19) and controls have been made available to develop machine-learning-based methods to aid in diagnosing the disease. However, these datasets are mainly composed of different sources coming from pre-COVID-19 datasets and COVID-19 datasets. Particularly, we have detected a significant bias in some of the released datasets used to train and test diagnostic systems, which might imply that the results published are optimistic and may overestimate the actual predictive capacity of the techniques proposed. In this article, we analyze the existing bias in some commonly used datasets and propose a series of preliminary steps to carry out before the classic machine learning pipeline in order to detect possible biases, to avoid them if possible and to report results that are more representative of the actual predictive power of the methods under analysis.
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Affiliation(s)
- Omar Del Tejo Catala
- Instituto Tecnológico de Informática (ITI), Universitat Politècnica de València 46022 Valencia Spain
| | - Ismael Salvador Igual
- Instituto Tecnológico de Informática (ITI), Universitat Politècnica de València 46022 Valencia Spain
| | | | - David Millan Escriva
- Instituto Tecnológico de Informática (ITI), Universitat Politècnica de València 46022 Valencia Spain
| | - Vicent Ortiz Castello
- Instituto Tecnológico de Informática (ITI), Universitat Politècnica de València 46022 Valencia Spain
| | - Rafael Llobet
- Instituto Tecnológico de Informática (ITI), Universitat Politècnica de València 46022 Valencia Spain
- Department of Computer Systems and Computation (DSIC)Universitat Politècnica de València 46022 Valencia Spain
| | - Juan-Carlos Perez-Cortes
- Instituto Tecnológico de Informática (ITI), Universitat Politècnica de València 46022 Valencia Spain
- Department of Computing Engineering (DISCA)Universitat Politècnica de València 46022 Valencia Spain
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