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      Artificial Intelligence and Digital Pathology: Challenges and Opportunities

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          Abstract

          In light of the recent success of artificial intelligence (AI) in computer vision applications, many researchers and physicians expect that AI would be able to assist in many tasks in digital pathology. Although opportunities are both manifest and tangible, there are clearly many challenges that need to be overcome in order to exploit the AI potentials in computational pathology. In this paper, we strive to provide a realistic account of all challenges and opportunities of adopting AI algorithms in digital pathology from both engineering and pathology perspectives.

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          Most cited references52

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          Deep Learning in Medical Image Analysis

          This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.
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            Predicting cancer outcomes from histology and genomics using convolutional networks

            Significance Predicting the expected outcome of patients diagnosed with cancer is a critical step in treatment. Advances in genomic and imaging technologies provide physicians with vast amounts of data, yet prognostication remains largely subjective, leading to suboptimal clinical management. We developed a computational approach based on deep learning to predict the overall survival of patients diagnosed with brain tumors from microscopic images of tissue biopsies and genomic biomarkers. This method uses adaptive feedback to simultaneously learn the visual patterns and molecular biomarkers associated with patient outcomes. Our approach surpasses the prognostic accuracy of human experts using the current clinical standard for classifying brain tumors and presents an innovative approach for objective, accurate, and integrated prediction of patient outcomes.
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              Systematic analysis of breast cancer morphology uncovers stromal features associated with survival.

              The morphological interpretation of histologic sections forms the basis of diagnosis and prognostication for cancer. In the diagnosis of carcinomas, pathologists perform a semiquantitative analysis of a small set of morphological features to determine the cancer's histologic grade. Physicians use histologic grade to inform their assessment of a carcinoma's aggressiveness and a patient's prognosis. Nevertheless, the determination of grade in breast cancer examines only a small set of morphological features of breast cancer epithelial cells, which has been largely unchanged since the 1920s. A comprehensive analysis of automatically quantitated morphological features could identify characteristics of prognostic relevance and provide an accurate and reproducible means for assessing prognosis from microscopic image data. We developed the C-Path (Computational Pathologist) system to measure a rich quantitative feature set from the breast cancer epithelium and stroma (6642 features), including both standard morphometric descriptors of image objects and higher-level contextual, relational, and global image features. These measurements were used to construct a prognostic model. We applied the C-Path system to microscopic images from two independent cohorts of breast cancer patients [from the Netherlands Cancer Institute (NKI) cohort, n = 248, and the Vancouver General Hospital (VGH) cohort, n = 328]. The prognostic model score generated by our system was strongly associated with overall survival in both the NKI and the VGH cohorts (both log-rank P ≤ 0.001). This association was independent of clinical, pathological, and molecular factors. Three stromal features were significantly associated with survival, and this association was stronger than the association of survival with epithelial characteristics in the model. These findings implicate stromal morphologic structure as a previously unrecognized prognostic determinant for breast cancer.
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                Author and article information

                Journal
                J Pathol Inform
                J Pathol Inform
                JPI
                Journal of Pathology Informatics
                Medknow Publications & Media Pvt Ltd (India )
                2229-5089
                2153-3539
                2018
                14 November 2018
                : 9
                : 38
                Affiliations
                [1 ]Kimia Lab, University of Waterloo, Canada
                [2 ]Huron Digital Pathology, Engineering Department, St. Jacobs, ON, Canada
                [3 ]Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA 15261, USA
                Author notes
                Address for correspondence: Prof. Hamid Reza Tizhoosh, Kimia Lab, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada. E-mail: tizhoosh@ 123456uwaterloo.ca
                Article
                JPI-9-38
                10.4103/jpi.jpi_53_18
                6289004
                30607305
                6653baab-7a53-472f-8302-470bca675a24
                Copyright: © 2018 Journal of Pathology Informatics

                This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

                History
                : 30 July 2018
                : 27 August 2018
                Categories
                Review Article

                Pathology
                artificial intelligence,deep learning,digital pathology
                Pathology
                artificial intelligence, deep learning, digital pathology

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