Intelligence artificielle : quel avenir en anatomie pathologique ?

Mahmud, 2018, Applications of deep learning and reinforcement learning to biological data, IEEE Trans Neural Netw Learn Syst, 29, 2063, 10.1109/TNNLS.2018.2790388 Cao, 2018, Deep learning and its applications in biomedicine, Genomics Proteomics Bioinformatics, 16, 17, 10.1016/j.gpb.2017.07.003 Rav, 2017, Deep learning for health informatics, IEEE J Biomed Health Inform, 21, 4, 10.1109/JBHI.2016.2636665 Jones, 2017, Computational biology: deep learning, Emerg Top Life Sci, 1, 257, 10.1042/ETLS20160025 Angermueller, 2016, Deep learning for computational biology, Mol Syst Biol, 12, 10.15252/msb.20156651 Min, 2016 Krizhevsky, 2012, 1097 Zeiler, 2013 Simonyan, 2014 Szegedy, 2014 Komura, 2018, Machine learning methods for histopathological image analysis, Computat Struct Biotechnol J, 16, 34, 10.1016/j.csbj.2018.01.001 Cruz-Roa, 2017, Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent, Scientic Reports, 7 Hamilton, 2014, Digital pathology and image analysis in tissue biomarker research, Methods, 70, 59, 10.1016/j.ymeth.2014.06.015 Janowczyk, 2016, Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases, J Pathol Inform, 7, 29, 10.4103/2153-3539.186902 Madabhushi, 2016, Image analysis and machine learning in digital pathology: challenges and opportunities, Med Image Anal, 33, 170, 10.1016/j.media.2016.06.037 Saha, 2016, Computer-aided diagnosis of breast cancer using cytological images: a systematic review, Tissue Cell, 48, 461, 10.1016/j.tice.2016.07.006 Saha, 2018, Ecient deep learning model for mitosis detection using breast histopathology images, Comput Med Imaging Graph, 64, 29, 10.1016/j.compmedimag.2017.12.001 Henriet, 2016, Apport diagnostique des reseaux de neurones articiels dans les tumeurs de Spitz de l’enfant et de l’adulte, Ann Dermatol Venereol, 143, S240, 10.1016/j.annder.2016.09.325 Devalland, 2017, Contribution to predict oncotype dx recurrence score from pathologic features in luminal b breast carcinoma using articial neural networks, ps-21-031, Virchows Arch Eur J Pathol, 471, 1 Zemouri, 2018, Constructive deep neural network for breast cancer diagnosis, Elsevier, ScienceDirect, 51, 98 Steiner, 2018, Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer, Am J Surg Pathol, 42, 1636, 10.1097/PAS.0000000000001151 Olsen, 2018, Diagnostic performance of deep learning algorithms applied to three common diagnoses in dermatopathology, J Pathol Inform, 9, 32, 10.4103/jpi.jpi_31_18 Xu, 2016, A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images, Neurocomputing, 191, 214, 10.1016/j.neucom.2016.01.034 Feng, 2018, Breast cancer cell nuclei classication in histopathology images using deep neural networks, Int J CARS, 13, 179, 10.1007/s11548-017-1663-9 Veta, 2016, Mitosis counting in breast cancer: object-level interobserver agreement and comparison to an automatic method, PLOS ONE, 11, 1, 10.1371/journal.pone.0161286 Vandenberghe, 2017, Relevance of deep learning to facilitate the diagnosis of her2 status in breast cancer, Scientic Rep, 7, 45938, 10.1038/srep45938 Grabe, 2018, Digitale pathologie in der immunonkologie-aktuelle chancen und herausforderungen, Pathologe, 39, 539, 10.1007/s00292-018-0540-9 Sheikhzadeh, 2018, Automatic labeling of molecular biomarkers of immunohistochemistry images using fully convolutional networks, PloS one, 13, 10.1371/journal.pone.0190783 Turkki, 2016, Antibody-supervised deep learning for quantication of tumor-inltrating immune cells in hematoxylin and eosin stained breast cancer samples, J Pathol Inform, 7, 38, 10.4103/2153-3539.189703 Granter, 2017, Straw men, deep learning, and the future of the human microscopist: response to articial intelligence and the pathologist: future frenemies?, Arch Pathol Lab Med, 141, 624, 10.5858/arpa.2017-0023-ED