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Braiki M, Nasreddine K, Benzinou A, Hymery N. Fuzzy Model for the Automatic Recognition of Human Dendritic Cells. J Imaging 2023; 9:jimaging9010013. [PMID: 36662111 PMCID: PMC9866805 DOI: 10.3390/jimaging9010013] [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: 09/16/2022] [Revised: 11/14/2022] [Accepted: 12/21/2022] [Indexed: 01/11/2023] Open
Abstract
Background and objective: Nowadays, foodborne illness is considered one of the most outgrowing diseases in the world, and studies show that its rate increases sharply each year. Foodborne illness is considered a public health problem which is caused by numerous factors, such as food intoxications, allergies, intolerances, etc. Mycotoxin is one of the food contaminants which is caused by various species of molds (or fungi), which, in turn, causes intoxications that can be chronic or acute. Thus, even low concentrations of Mycotoxin have a severely harmful impact on human health. It is, therefore, necessary to develop an assessment tool for evaluating their impact on the immune response. Recently, researchers have approved a new method of investigation using human dendritic cells, yet the analysis of the geometric properties of these cells is still visual. Moreover, this type of analysis is subjective, time-consuming, and difficult to perform manually. In this paper, we address the automation of this evaluation using image-processing techniques. Methods: Automatic classification approaches of microscopic dendritic cell images are developed to provide a fast and objective evaluation. The first proposed classifier is based on support vector machines (SVM) and Fisher's linear discriminant analysis (FLD) method. The FLD-SVM classifier does not provide satisfactory results due to the significant confusion between the inhibited cells on one hand, and the other two cell types (mature and immature) on the other hand. Then, another strategy was suggested to enhance dendritic cell recognition results that are emitted from microscopic images. This strategy is mainly based on fuzzy logic which allows us to consider the uncertainties and inaccuracies of the given data. Results: These proposed methods are tested on a real dataset consisting of 421 images of microscopic dendritic cells, where the fuzzy classification scheme efficiently improved the classification results by successfully classifying 96.77% of the dendritic cells. Conclusions: The fuzzy classification-based tools provide cell maturity and inhibition rates which help biologists evaluate severe health impacts caused by food contaminants.
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Affiliation(s)
- Marwa Braiki
- ENIB, UMR CNRS 6285 LabSTICC, 29238 Brest, France
| | - Kamal Nasreddine
- ENIB, UMR CNRS 6285 LabSTICC, 29238 Brest, France
- Correspondence:
| | | | - Nolwenn Hymery
- Univ Brest, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, 29280 Plouzané, France
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Foran DJ, Durbin EB, Chen W, Sadimin E, Sharma A, Banerjee I, Kurc T, Li N, Stroup AM, Harris G, Gu A, Schymura M, Gupta R, Bremer E, Balsamo J, DiPrima T, Wang F, Abousamra S, Samaras D, Hands I, Ward K, Saltz JH. An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools, and Advanced Mining Capabilities. J Pathol Inform 2022; 13:5. [PMID: 35136672 PMCID: PMC8794027 DOI: 10.4103/jpi.jpi_31_21] [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: 04/30/2021] [Accepted: 04/30/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI's Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features). MATERIALS AND METHODS As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated. RESULTS Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics. CONCLUSION To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures.
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Affiliation(s)
- David J. Foran
- Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
- Department of Pathology and Laboratory Medicine, Rutgers-Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Eric B. Durbin
- Kentucky Cancer Registry, Markey Cancer Center, University of Kentucky, Lexington, KY, USA
- Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine, Lexington, KY, USA
| | - Wenjin Chen
- Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Evita Sadimin
- Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
- Department of Pathology and Laboratory Medicine, Rutgers-Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Imon Banerjee
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Nan Li
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Antoinette M. Stroup
- New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Gerald Harris
- New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Annie Gu
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Maria Schymura
- New York State Cancer Registry, New York State Department of Health, Albany, NY, USA
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Erich Bremer
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Joseph Balsamo
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Tammy DiPrima
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Feiqiao Wang
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Shahira Abousamra
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Isaac Hands
- Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine, Lexington, KY, USA
| | - Kevin Ward
- Georgia State Cancer Registry, Georgia Department of Public Health, Atlanta, GA, USA
| | - Joel H. Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
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Color clustering segmentation framework for image analysis of malignant lymphoid cells in peripheral blood. Med Biol Eng Comput 2019; 57:1265-1283. [DOI: 10.1007/s11517-019-01954-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 01/19/2019] [Indexed: 10/27/2022]
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Rodellar J, Alférez S, Acevedo A, Molina A, Merino A. Image processing and machine learning in the morphological analysis of blood cells. Int J Lab Hematol 2018; 40 Suppl 1:46-53. [PMID: 29741258 DOI: 10.1111/ijlh.12818] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 02/21/2018] [Indexed: 01/21/2023]
Abstract
INTRODUCTION This review focuses on how image processing and machine learning can be useful for the morphological characterization and automatic recognition of cell images captured from peripheral blood smears. METHODS The basics of the 3 core elements (segmentation, quantitative features, and classification) are outlined, and recent literature is discussed. Although red blood cells are a significant part of this context, this study focuses on malignant lymphoid cells and blast cells. RESULTS There is no doubt that these technologies may help the cytologist to perform efficient, objective, and fast morphological analysis of blood cells. They may also help in the interpretation of some morphological features and may serve as learning and survey tools. CONCLUSION Although research is still needed, it is important to define screening strategies to exploit the potential of image-based automatic recognition systems integrated in the daily routine of laboratories along with other analysis methodologies.
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Affiliation(s)
- J Rodellar
- Department of Mathematics, Barcelona Est Engineering School, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - S Alférez
- Department of Mathematics, Barcelona Est Engineering School, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - A Acevedo
- Department of Mathematics, Barcelona Est Engineering School, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - A Molina
- Biomedical Diagnostic Center Core Laboratory, Hospital Clínic, Barcelona, Spain
| | - A Merino
- Biomedical Diagnostic Center Core Laboratory, Hospital Clínic, Barcelona, Spain
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Roberto GF, Neves LA, Nascimento MZ, Tosta TA, Longo LC, Martins AS, Faria PR. Features based on the percolation theory for quantification of non-Hodgkin lymphomas. Comput Biol Med 2017; 91:135-147. [DOI: 10.1016/j.compbiomed.2017.10.012] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Revised: 10/11/2017] [Accepted: 10/12/2017] [Indexed: 11/26/2022]
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A survey for the applications of content-based microscopic image analysis in microorganism classification domains. Artif Intell Rev 2017. [DOI: 10.1007/s10462-017-9572-4] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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Alférez S, Merino A, Bigorra L, Rodellar J. Characterization and automatic screening of reactive and abnormal neoplastic B lymphoid cells from peripheral blood. Int J Lab Hematol 2016; 38:209-19. [PMID: 26995648 DOI: 10.1111/ijlh.12473] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Accepted: 11/20/2015] [Indexed: 11/30/2022]
Abstract
INTRODUCTION The objective was to advance in the automatic, image-based, characterization and recognition of a heterogeneous set of lymphoid cells from peripheral blood, including normal, reactive, and five groups of abnormal lymphocytes: hairy cells, mantle cells, follicular lymphoma, chronic lymphocytic leukemia, and prolymphocytes. METHODS A number of 4389 images from 105 patients were selected by pathologists, based on morphologic visual appearance, from patients whose diagnosis was confirmed by all the remaining complementary tests. Besides geometry, new color and texture features were extracted using six alternative color spaces to obtain rich information to characterize the cell groups. The recognition system was designed using support vector machines trained with the whole image set. RESULTS In the experimental tests, individual sets of images from 21 new patients were analyzed by the trained recognition system and compared with the true diagnosis. An overall recognition accuracy of 97.67% was achieved when the cell screening was performed into three groups: normal lymphocytes, abnormal lymphoid cells, and reactive lymphocytes. The accuracy of the whole experimental study was 91.23% when considering the further discrimination of the abnormal lymphoid cells into the specific five groups. CONCLUSION The excellent automatic screening of the three groups of normal, reactive, and abnormal lymphocytes is useful as it discriminates between malignancy and not malignancy. The discrimination of the five groups of abnormal lymphoid cells is encouraging toward the idea that the system could be an automated image-based screening method to identify blood involvement by a variety of B lymphomas.
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Affiliation(s)
- S Alférez
- Matematica Aplicada III, Technical University of Catalonia, Barcelona, Spain
| | - A Merino
- Department of Hemotherapy-Hemostasis, Hospital Clinic, Barcelona, Spain
| | - L Bigorra
- Matematica Aplicada III, Technical University of Catalonia, Barcelona, Spain.,Department of Hemotherapy-Hemostasis, Hospital Clinic, Barcelona, Spain
| | - J Rodellar
- Matematica Aplicada III, Technical University of Catalonia, Barcelona, Spain
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Jiang M, Zhang S, Huang J, Yang L, Metaxas DN. Scalable histopathological image analysis via supervised hashing with multiple features. Med Image Anal 2016; 34:3-12. [PMID: 27521299 DOI: 10.1016/j.media.2016.07.011] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Revised: 04/08/2016] [Accepted: 07/28/2016] [Indexed: 11/18/2022]
Abstract
Histopathology is crucial to diagnosis of cancer, yet its interpretation is tedious and challenging. To facilitate this procedure, content-based image retrieval methods have been developed as case-based reasoning tools. Especially, with the rapid growth of digital histopathology, hashing-based retrieval approaches are gaining popularity due to their exceptional efficiency and scalability. Nevertheless, few hashing-based histopathological image analysis methods perform feature fusion, despite the fact that it is a common practice to improve image retrieval performance. In response, we exploit joint kernel-based supervised hashing (JKSH) to integrate complementary features in a hashing framework. Specifically, hashing functions are designed based on linearly combined kernel functions associated with individual features. Supervised information is incorporated to bridge the semantic gap between low-level features and high-level diagnosis. An alternating optimization method is utilized to learn the kernel combination and hashing functions. The obtained hashing functions compress multiple high-dimensional features into tens of binary bits, enabling fast retrieval from a large database. Our approach is extensively validated on 3121 breast-tissue histopathological images by distinguishing between actionable and benign cases. It achieves 88.1% retrieval precision and 91.3% classification accuracy within 16.5 ms query time, comparing favorably with traditional methods.
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Affiliation(s)
- Menglin Jiang
- Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA
| | - Shaoting Zhang
- Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
| | - Junzhou Huang
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
| | - Lin Yang
- Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Dimitris N Metaxas
- Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA
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Bigorra L, Merino A, Alférez S, Rodellar J. Feature Analysis and Automatic Identification of Leukemic Lineage Blast Cells and Reactive Lymphoid Cells from Peripheral Blood Cell Images. J Clin Lab Anal 2016; 31. [PMID: 27427422 DOI: 10.1002/jcla.22024] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Accepted: 06/09/2016] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Automated peripheral blood (PB) image analyzers usually underestimate the total number of blast cells, mixing them up with reactive or normal lymphocytes. Therefore, they are not able to discriminate between myeloid or lymphoid blast cell lineages. The objective of the proposed work is to achieve automatic discrimination of reactive lymphoid cells (RLC), lymphoid and myeloid blast cells and to obtain their morphologic patterns through feature analysis. METHODS In the training stage, a set of 696 blood cell images was selected in 32 patients (myeloid acute leukemia, lymphoid precursor neoplasms and viral or other infections). For classification, we used support vector machines, testing different combinations of feature categories and feature selection techniques. Further, a validation was implemented using the selected features over 220 images from 15 new patients (five corresponding to each category). RESULTS Best discrimination accuracy in the training was obtained with feature selection from the whole feature set (90.1%). We selected 60 features, showing significant differences (P < 0.001) in the mean values of the different cell groups. Nucleus-cytoplasm ratio was the most important feature for the cell classification, and color-texture features from the cytoplasm were also important. In the validation stage, the overall classification accuracy and the true-positive rates for RLC, myeloid and lymphoid blast cells were 80%, 85%, 82% and 74%, respectively. CONCLUSION The methodology appears to be able to recognize reactive lymphocytes well, especially between reactive lymphocytes and lymphoblasts.
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Affiliation(s)
- Laura Bigorra
- Hemotherapy-Hemostasis, Hospital Clinic de Barcelona, CDB, Barcelona, Spain.,CoDAlab, Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Anna Merino
- Hemotherapy-Hemostasis, Hospital Clinic de Barcelona, CDB, Barcelona, Spain
| | | | - José Rodellar
- CoDAlab, Universitat Politecnica de Catalunya, Barcelona, Spain
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Xing F, Yang L. Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review. IEEE Rev Biomed Eng 2016; 9:234-63. [PMID: 26742143 PMCID: PMC5233461 DOI: 10.1109/rbme.2016.2515127] [Citation(s) in RCA: 213] [Impact Index Per Article: 26.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.
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Peikari M, Gangeh MJ, Zubovits J, Clarke G, Martel AL. Triaging Diagnostically Relevant Regions from Pathology Whole Slides of Breast Cancer: A Texture Based Approach. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:307-315. [PMID: 26302511 DOI: 10.1109/tmi.2015.2470529] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
PURPOSE Pathologists often look at whole slide images (WSIs) at low magnification to find potentially important regions and then zoom in to higher magnification to perform more sophisticated analysis of the tissue structures. Many automated methods of WSI analysis attempt to preprocess the down-sampled image in order to select salient regions which are then further analyzed by a more computationally intensive step at full magnification. Although it can greatly reduce processing times, this process may lead to small potentially important regions being overlooked at low magnification. We propose a texture analysis technique to ease the processing of H&E stained WSIs by triaging clinically important regions. METHOD Image patches randomly selected from the whole tissue area were divided into smaller tiles and Gaussian-like texture filters were applied to them. Texture filter responses from each tile were combined together and statistical measures were derived from their histograms of responses. Bag of visual words pipeline was then employed to combine extracted features from tiles to form one histogram of words per every image patch. A support vector machine classifier was trained using the calculated histograms of words to be able to distinguish between clinically relevant and irrelevant patches. RESULT Experimental analysis on 5151 image patches from 10 patient cases (65 tissue slides) indicated that our proposed texture technique out-performed two previously proposed colour and intensity based methods with an area under the ROC curve of 0.87. CONCLUSION Texture features can be employed to triage clinically important areas within large WSIs.
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Barker J, Hoogi A, Depeursinge A, Rubin DL. Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles. Med Image Anal 2015; 30:60-71. [PMID: 26854941 DOI: 10.1016/j.media.2015.12.002] [Citation(s) in RCA: 107] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Revised: 12/02/2015] [Accepted: 12/07/2015] [Indexed: 02/07/2023]
Abstract
Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1% (p << 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p << 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes.
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Affiliation(s)
- Jocelyn Barker
- Department of Medicine (Stanford Biomedical Informatics Research), Stanford University School of Medicine, CA, USA.
| | - Assaf Hoogi
- Department of Radiology, Stanford University School of Medicine, CA, USA.
| | - Adrien Depeursinge
- Department of Radiology, Stanford University School of Medicine, CA, USA; Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland.
| | - Daniel L Rubin
- Department of Radiology, Stanford University School of Medicine, CA, USA; Department of Medicine (Stanford Biomedical Informatics Research), Stanford University School of Medicine, CA, USA.
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Frequential versus spatial colour textons for breast TMA classification. Comput Med Imaging Graph 2015; 42:25-37. [DOI: 10.1016/j.compmedimag.2014.11.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2014] [Revised: 06/30/2014] [Accepted: 11/10/2014] [Indexed: 11/19/2022]
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Alférez S, Merino A, Bigorra L, Mujica L, Ruiz M, Rodellar J. Automatic recognition of atypical lymphoid cells from peripheral blood by digital image analysis. Am J Clin Pathol 2015; 143:168-76; quiz 305. [PMID: 25596242 DOI: 10.1309/ajcp78ifstogzzjn] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
Abstract
OBJECTIVES The objective was the development of a method for the automatic recognition of different types of atypical lymphoid cells. METHODS In the method development, a training set (TS) of 1,500 lymphoid cell images from peripheral blood was used. To segment the images, we used clustering of color components and watershed transformation. In total, 113 features were extracted for lymphocyte recognition by linear discriminant analysis (LDA) with a 10-fold cross-validation over the TS. Then, a new validation set (VS) of 150 images was used, performing two steps: (1) tuning the LDA classifier using the TS and (2) classifying the VS in the different lymphoid cell types. RESULTS The segmentation algorithm was very effective in separating the cytoplasm, nucleus, and peripheral zone around the cell. From them, descriptive features were extracted and used to recognize the different lymphoid cells. The accuracy for the classification in the TS was 98.07%. The precision, sensitivity, and specificity values were above 99.7%, 97.5%, and 98.6%, respectively. The accuracy of the classification in the VS was 85.33%. CONCLUSIONS The method reaches a high precision in the recognition of five different types of lymphoid cells and could allow for the design of a diagnosis support tool in the future.
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Affiliation(s)
| | - Anna Merino
- Department of Hemotherapy-Hemostasis, Hospital Clinic, Barcelona, Spain
| | - Laura Bigorra
- Universitat Politècnica de Catalunya, Barcelona, Spain
- Department of Hemotherapy-Hemostasis, Hospital Clinic, Barcelona, Spain
| | - Luis Mujica
- Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Magda Ruiz
- Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Jose Rodellar
- Universitat Politècnica de Catalunya, Barcelona, Spain
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Zhang X, Liu W, Dundar M, Badve S, Zhang S. Towards large-scale histopathological image analysis: hashing-based image retrieval. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:496-506. [PMID: 25314696 DOI: 10.1109/tmi.2014.2361481] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Automatic analysis of histopathological images has been widely utilized leveraging computational image-processing methods and modern machine learning techniques. Both computer-aided diagnosis (CAD) and content-based image-retrieval (CBIR) systems have been successfully developed for diagnosis, disease detection, and decision support in this area. Recently, with the ever-increasing amount of annotated medical data, large-scale and data-driven methods have emerged to offer a promise of bridging the semantic gap between images and diagnostic information. In this paper, we focus on developing scalable image-retrieval techniques to cope intelligently with massive histopathological images. Specifically, we present a supervised kernel hashing technique which leverages a small amount of supervised information in learning to compress a 10 000-dimensional image feature vector into only tens of binary bits with the informative signatures preserved. These binary codes are then indexed into a hash table that enables real-time retrieval of images in a large database. Critically, the supervised information is employed to bridge the semantic gap between low-level image features and high-level diagnostic information. We build a scalable image-retrieval framework based on the supervised hashing technique and validate its performance on several thousand histopathological images acquired from breast microscopic tissues. Extensive evaluations are carried out in terms of image classification (i.e., benign versus actionable categorization) and retrieval tests. Our framework achieves about 88.1% classification accuracy as well as promising time efficiency. For example, the framework can execute around 800 queries in only 0.01 s, comparing favorably with other commonly used dimensionality reduction and feature selection methods.
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Automatic classification of atypical lymphoid B cells using digital blood image processing. Int J Lab Hematol 2013; 36:472-80. [DOI: 10.1111/ijlh.12175] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2013] [Accepted: 10/25/2013] [Indexed: 11/26/2022]
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Gensure RH, Foran DJ, Lee VM, Gendel VM, Jabbour SK, Carpizo DR, Nosher JL, Yang L. Evaluation of hepatic tumor response to yttrium-90 radioembolization therapy using texture signatures generated from contrast-enhanced CT images. Acad Radiol 2012; 19:1201-7. [PMID: 22841288 DOI: 10.1016/j.acra.2012.04.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2012] [Revised: 04/26/2012] [Accepted: 04/26/2012] [Indexed: 10/28/2022]
Abstract
RATIONALE AND OBJECTIVES The aim of this study was to explore the use of texture features generated from liver computed tomographic (CT) datasets as potential image-based indicators of patient response to radioembolization (RE) with yttrium-90 ((90)Y) resin microspheres, an emerging locoregional therapy for advanced-stage liver cancer. MATERIALS AND METHODS Overall posttherapy survival and percent change in serologic tumor marker at 3 months posttherapy represent the primary clinical outcomes in this study. Thirty advanced-stage liver cancer cases (primary and metastatic) treated with RE over a 3-year period were included. Texture signatures for tumor regions, which were delineated to reveal boundaries with normal regions, were computed from pretreatment contrast-enhanced liver CT studies and evaluated for their ability to classify patient serologic response and survival. RESULTS A series of systematic leave-one-out cross-validation studies using soft-margin support vector machine (SVM) classifiers showed hepatic tumor texton and local binary pattern (LBP) signatures both achieve high accuracy (96%) in discriminating subjects in terms of their serologic response. The image-based indicators were also accurate in classifying subjects by survival status (80% and 93% accuracy for texton and LBP signatures, respectively). CONCLUSIONS Hepatic texture signatures generated from tumor regions on pretreatment triphasic CT studies were highly accurate in differentiating among subjects in terms of serologic response and survival. These image-based computational markers show promise as potential predictive tools in candidate evaluation for locoregional therapy such as RE.
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Extraction of nucleolus candidate zone in white blood cells of peripheral blood smear images using curvelet transform. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2012; 2012:574184. [PMID: 22666305 PMCID: PMC3361198 DOI: 10.1155/2012/574184] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2011] [Revised: 02/08/2012] [Accepted: 02/11/2012] [Indexed: 11/23/2022]
Abstract
The main part of each white blood cell (WBC) is its nucleus which contains chromosomes. Although white blood cells (WBCs) with giant nuclei are the main symptom of leukemia, they are not sufficient to prove this disease and other symptoms must be investigated. For example another important symptom of leukemia is the existence of nucleolus in nucleus. The nucleus contains chromatin and a structure called the nucleolus. Chromatin is DNA in its active form while nucleolus is composed of protein and RNA, which are usually inactive. In this paper, to diagnose this symptom and in order to discriminate between nucleoli and chromatins, we employ curvelet transform, which is a multiresolution transform for detecting 2D singularities in images. For this reason, at first nuclei are extracted by means of K-means method, then curvelet transform is applied on extracted nuclei and the coefficients are modified, and finally reconstructed image is used to extract the candidate locations of chromatins and nucleoli. This method is applied on 100 microscopic images and succeeds with specificity of 80.2% and sensitivity of 84.3% to detect the nucleolus candidate zone. After nucleolus candidate zone detection, new features that can be used to classify atypical and blast cells such as gradient of saturation channel are extracted.
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Orlov NV, Weeraratna AT, Hewitt SM, Coletta CE, Delaney JD, Mark Eckley D, Shamir L, Goldberg IG. Automatic detection of melanoma progression by histological analysis of secondary sites. Cytometry A 2012; 81:364-73. [PMID: 22467531 PMCID: PMC3331954 DOI: 10.1002/cyto.a.22044] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2011] [Revised: 02/23/2012] [Accepted: 02/29/2012] [Indexed: 11/10/2022]
Abstract
We present results from machine classification of melanoma biopsies sectioned and stained with hematoxylin/eosin (H&E) on tissue microarrays (TMA). The four stages of melanoma progression were represented by seven tissue types, including benign nevus, primary tumors with radial and vertical growth patterns (stage I) and four secondary metastatic tumors: subcutaneous (stage II), lymph node (stage III), gastrointestinal and soft tissue (stage IV). Our experiment setup comprised 14,208 image samples based on 164 TMA cores. In our experiments, we constructed an HE color space by digitally deconvolving the RGB images into separate H (hematoxylin) and E (eosin) channels. We also compared three different classifiers: Weighted Neighbor Distance (WND), Radial Basis Functions (RBF), and k-Nearest Neighbors (kNN). We found that the HE color space consistently outperformed other color spaces with all three classifiers, while the different classifiers did not have as large of an effect on accuracy. This showed that a more physiologically relevant representation of color can have a larger effect on correct image interpretation than downstream processing steps. We were able to correctly classify individual fields of view with an average of 96% accuracy when randomly splitting the dataset into training and test fields. We also obtained a classification accuracy of 100% when testing entire cores that were not previously used in training (four random trials with one test core for each of 7 classes, 28 tests total). Because each core corresponded to a different patient, this test more closely mimics a clinically relevant setting where new patients are evaluated based on training with previous cases. The analysis method used in this study contains no parameters or adjustments that are specific to melanoma morphology, suggesting it can be used for analyzing other tissues and phenotypes, as well as potentially different image modalities and contrast techniques.
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Affiliation(s)
- Nikita V Orlov
- National Institution on Aging, NIH, Laboratory of Genetics, Baltimore, Maryland, USA.
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Sertel O, Dogdas B, Chiu CS, Gurcan MN. Microscopic image analysis for quantitative characterization of muscle fiber type composition. Comput Med Imaging Graph 2011; 35:616-28. [PMID: 21342753 DOI: 10.1016/j.compmedimag.2011.01.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2010] [Revised: 01/27/2011] [Accepted: 01/27/2011] [Indexed: 10/18/2022]
Abstract
Skeletal muscles consist of muscle fibers that are responsible for contracting and generating force. Skeletal muscle fibers are categorized into distinct subtypes based on several characteristics such as contraction time, force production and resistance to fatigue. The composition of distinct muscle fibers in terms of their number and cross-sectional areas is characterized by a histological examination. However, manual delineation of individual muscle fibers from digitized muscle histology tissue sections is extremely time-consuming. In this study, we propose an automated image analysis system for quantitative characterization of muscle fiber type composition. The proposed system operates on digitized histological muscle tissue slides and consists of the following steps: segmentation of muscle fibers, registration of successive slides with distinct stains, and classification of muscle fibers into distinct subtypes. The performance of the proposed approach was tested on a dataset consisting of 25 image pairs of successive muscle histological cross-sections with different ATPase stain. Experimental results demonstrate a promising overall segmentation and classification accuracy of 89.1% in identifying muscle fibers of distinct subtypes.
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Affiliation(s)
- Olcay Sertel
- Dept. of Biomedical Informatics, The Ohio State Univ., Columbus, OH, USA.
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21
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Orlov NV, Chen WW, Eckley DM, Macura TJ, Shamir L, Jaffe ES, Goldberg IG. Automatic classification of lymphoma images with transform-based global features. ACTA ACUST UNITED AC 2010; 14:1003-13. [PMID: 20659835 DOI: 10.1109/titb.2010.2050695] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We propose a report on automatic classification of three common types of malignant lymphoma: chronic lymphocytic leukemia, follicular lymphoma, and mantle cell lymphoma. The goal was to find patterns indicative of lymphoma malignancies and allowing classifying these malignancies by type. We used a computer vision approach for quantitative characterization of image content. A unique two-stage approach was employed in this study. At the outer level, raw pixels were transformed with a set of transforms into spectral planes. Simple (Fourier, Chebyshev, and wavelets) and compound transforms (Chebyshev of Fourier and wavelets of Fourier) were computed. Raw pixels and spectral planes were then routed to the second stage (the inner level). At the inner level, the set of multipurpose global features was computed on each spectral plane by the same feature bank. All computed features were fused into a single feature vector. The specimens were stained with hematoxylin (H) and eosin (E) stains. Several color spaces were used: RGB, gray, CIE-L*a*b*, and also the specific stain-attributed H&E space, and experiments on image classification were carried out for these sets. The best signal (98%-99% on earlier unseen images) was found for the HE, H, and E channels of the H&E data set.
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Affiliation(s)
- Nikita V Orlov
- National Institute on Aging, NIH, Baltimore, MD 21224, USA.
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Soda P, Onofri L, Iannello G. A decision support system for Crithidia luciliae image classification. Artif Intell Med 2010; 51:67-74. [PMID: 20630721 DOI: 10.1016/j.artmed.2010.05.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2009] [Revised: 05/14/2010] [Accepted: 05/18/2010] [Indexed: 10/19/2022]
Abstract
OBJECTIVE Systemic lupus erythematosus is a connective tissue disease affecting multiple organ systems and characterised by a chronic inflammatory process. It is considered a very serious sickness, further to be classified as an invalidating chronic disease. The recommended method for its detection is the indirect immunofluorescence (IIF) based on Crithidia Luciliae (CL) substrate. Hoverer, IIF is affected by several issues limiting tests reliability and reproducibility. Hence, an evident medical demand is the development of computer-aided diagnosis tools that can offer a support to physician decision. METHODS In this paper we propose a system that classifies CL wells integrating information extracted from different images. It is based on three main decision phases. Two steps, named as threshold-based classification and single cells recognition, are applied for image classification. They minimise false negative and false positive classifications, respectively. Feature extraction and selection have been carried out to determine a compact set of descriptors to distinguish between positive and negative cells. The third step applies majority voting rule at well recognition level, enabling us to recover possible errors provided by previous phases. RESULTS The system performance have been evaluated on an annotated database of IIF CL wells, composed of 63 wells for a total of 342 images and 1487 cells. Accuracy, sensitivity and specificity of image recognition step are 99.4%, 98.6% and 99.6%, respectively. At level of well recognition, accuracy, sensitivity and specificity are 98.4%, 93.3% and 100.0%, respectively. The system has been also validated in a daily routine fashion on 48 consecutive analyses of hospital outpatients and inpatients. The results show very good performance for well recognition (100% of accuracy, sensitivity and specificity), due to the integration of cells and images information. CONCLUSIONS The described recognition system can be applied in daily routine in order to improve the reliability, standardisation and reproducibility of CL readings in IIF.
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Affiliation(s)
- Paolo Soda
- Medical Informatics and Computer Science Laboratory, Integrated Research Centre, University Campus Bio-Medico of Rome, Via Alvaro del Portillo, 21, 00128 Rome, Italy.
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Basavanhally AN, Ganesan S, Agner S, Monaco JP, Feldman MD, Tomaszewski JE, Bhanot G, Madabhushi A. Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology. IEEE Trans Biomed Eng 2009; 57:642-53. [PMID: 19884074 DOI: 10.1109/tbme.2009.2035305] [Citation(s) in RCA: 189] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The identification of phenotypic changes in breast cancer (BC) histopathology on account of corresponding molecular changes is of significant clinical importance in predicting disease outcome. One such example is the presence of lymphocytic infiltration (LI) in histopathology, which has been correlated with nodal metastasis and distant recurrence in HER2+ BC patients. In this paper, we present a computer-aided diagnosis (CADx) scheme to automatically detect and grade the extent of LI in digitized HER2+ BC histopathology. Lymphocytes are first automatically detected by a combination of region growing and Markov random field algorithms. Using the centers of individual detected lymphocytes as vertices, three graphs (Voronoi diagram, Delaunay triangulation, and minimum spanning tree) are constructed and a total of 50 image-derived features describing the arrangement of the lymphocytes are extracted from each sample. A nonlinear dimensionality reduction scheme, graph embedding (GE), is then used to project the high-dimensional feature vector into a reduced 3-D embedding space. A support vector machine classifier is used to discriminate samples with high and low LI in the reduced dimensional embedding space. A total of 41 HER2+ hematoxylin-and-eosin-stained images obtained from 12 patients were considered in this study. For more than 100 three-fold cross-validation trials, the architectural feature set successfully distinguished samples of high and low LI levels with a classification accuracy greater than 90%. The popular unsupervised Varma-Zisserman texton-based classification scheme was used for comparison and yielded a classification accuracy of only 60%. Additionally, the projection of the 50 image-derived features for all 41 tissue samples into a reduced dimensional space via GE allowed for the visualization of a smooth manifold that revealed a continuum between low, intermediate, and high levels of LI. Since it is known that extent of LI in BC biopsy specimens is a prognostic indicator, our CADx scheme will potentially help clinicians determine disease outcome and allow them to make better therapy recommendations for patients with HER2+ BC.
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Yang L, Tuzel O, Chen W, Meer P, Salaru G, Goodell LA, Foran DJ. PathMiner: a Web-based tool for computer-assisted diagnostics in pathology. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE : A PUBLICATION OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY 2009; 13:291-9. [PMID: 19171530 PMCID: PMC3683402 DOI: 10.1109/titb.2008.2008801] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Large-scale, multisite collaboration has become indispensable for a wide range of research and clinical activities that rely on the capacity of individuals to dynamically acquire, share, and assess images and correlated data. In this paper, we report the development of a Web-based system, PathMiner , for interactive telemedicine, intelligent archiving, and automated decision support in pathology. The PathMiner system supports network-based submission of queries and can automatically locate and retrieve digitized pathology specimens along with correlated molecular studies of cases from "ground-truth" databases that exhibit spectral and spatial profiles consistent with a given query image. The statistically most probable diagnosis is provided to the individual who is seeking decision support. To test the system under real-case scenarios, a pipeline infrastructure was developed and a network-based test laboratory was established at strategic sites at the University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, Robert Wood Johnson University Hospital, the University of Pennsylvania School of Medicine, Hospital of the University of Pennsylvania, The Cancer Institute of New Jersey, and Rutgers University. The average five-class classification accuracy of the system was 93.18% based on a tenfold cross validation on a close dataset containing 3691 imaged specimens. We also conducted prospective performance studies with the PathMiner system in real applications in which the specimens exhibited large variations in staining characters compared with the training data. The average five-class classification accuracy in this open-set experiment was 87.22%. We also provide the comparative results with the previous literature and the PathMiner system shows superior performance.
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Affiliation(s)
- Lin Yang
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, USA.
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Soda P, Iannello G, Vento M. A multiple expert system for classifying fluorescent intensity in antinuclear autoantibodies analysis. Pattern Anal Appl 2008. [DOI: 10.1007/s10044-008-0116-z] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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