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Predicting mutational status of driver and suppressor genes directly from histopathology with Deep Learning: A systematic study across 23 solid tumor types

C. Loeffler, N. Gaisa, H. Muti, M. van Treeck, A. Echle, N. Ghaffari Laleh, C. Trautwein, L. Heij, H. Grabsch, N. Ortiz Bruechle, and J. Kather. Front. Genet., (2021)

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Medical domain knowledge in domain-agnostic generative AIJ. Kather, N. Ghaffari Laleh, S. Foersch, and D. Truhn. NPJ Digit. Med., 5 (1): 90 (July 2022)Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancerS. Brockmoeller, A. Echle, N. Ghaffari Laleh, S. Eiholm, M. Malmstrøm, T. Plato Kuhlmann, K. Levic, H. Grabsch, N. West, O. Saldanha and 7 other author(s). The Journal of Pathology, 256 (3): 269–281 (December 2021)Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancerS. Brockmoeller, A. Echle, N. Ghaffari Laleh, S. Eiholm, M. Malmstrøm, T. Plato Kuhlmann, K. Levic, H. Grabsch, N. West, O. Saldanha and 7 other author(s). J. Pathol., 256 (3): 269--281 (March 2022)Swarm learning for decentralized artificial intelligence in cancer histopathologyO. Saldanha, P. Quirke, N. West, J. James, M. Loughrey, H. Grabsch, M. Salto-Tellez, E. Alwers, D. Cifci, N. Ghaffari Laleh and 17 other author(s). Nat. Med., 28 (6): 1232--1239 (June 2022)Classical mathematical models for prediction of response to chemotherapy and immunotherapyN. Ghaffari Laleh, C. Loeffler, J. Grajek, K. Stanková, A. Pearson, H. Muti, C. Trautwein, H. Enderling, J. Poleszczuk, and J. Kather. PLoS Comput. Biol., 18 (2): e1009822 (February 2022)Predicting mutational status of driver and suppressor genes directly from histopathology with Deep Learning: A systematic study across 23 solid tumor typesC. Loeffler, N. Gaisa, H. Muti, M. van Treeck, A. Echle, N. Ghaffari Laleh, C. Trautwein, L. Heij, H. Grabsch, N. Ortiz Bruechle and 1 other author(s). Front. Genet., (2021)Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathologyN. Ghaffari Laleh, H. Muti, C. Loeffler, A. Echle, O. Saldanha, F. Mahmood, M. Lu, C. Trautwein, R. Langer, B. Dislich and 13 other author(s). Med. Image Anal., 79 (102474): 102474 (July 2022)Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: A possible preselection for molecular testing?C. Loeffler, N. Ortiz Bruechle, M. Jung, L. Seillier, M. Rose, N. Laleh, R. Knuechel, T. Brinker, C. Trautwein, N. Gaisa and 1 other author(s). Eur. Urol. Focus, 8 (2): 472--479 (March 2022)Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathologyP. Schrammen, N. Ghaffari Laleh, A. Echle, D. Truhn, V. Schulz, T. Brinker, H. Brenner, J. Chang-Claude, E. Alwers, A. Brobeil and 9 other author(s). J. Pathol., 256 (1): 50--60 (January 2022)Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histologyQ. Zeng, C. Klein, S. Caruso, P. Maille, N. Laleh, D. Sommacale, A. Laurent, G. Amaddeo, D. Gentien, A. Rapinat and 10 other author(s). J. Hepatol., 77 (1): 116--127 (July 2022)

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Predicting mutational status of driver and suppressor genes directly from histopathology with Deep Learning: A systematic study across 23 solid tumor types

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