Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study

[1]  Jakob Nikolas Kather,et al.  Multimodal deep learning: An improvement in prognostication or a reflection of batch effect? , 2022, Cancer cell.

[2]  Jakob Nikolas Kather,et al.  Deep learning can predict multi-omic biomarkers from routine pathology images: A systematic large-scale study , 2022, bioRxiv.

[3]  Junzhou Huang,et al.  RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval , 2022, Medical Image Anal..

[4]  Jakob Nikolas Kather,et al.  Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning , 2022, medRxiv.

[5]  Jakob Nikolas Kather,et al.  Self-supervised deep learning for pan-cancer mutation prediction from histopathology , 2022, bioRxiv.

[6]  Jakob Nikolas Kather,et al.  Artificial intelligence in histopathology: enhancing cancer research and clinical oncology , 2022, Nature Cancer.

[7]  J. Ajani,et al.  Association of PD-L1 Expression and Other Variables With Benefit From Immune Checkpoint Inhibition in Advanced Gastroesophageal Cancer: Systematic Review and Meta-analysis of 17 Phase 3 Randomized Clinical Trials. , 2022, JAMA oncology.

[8]  Ming Y. Lu,et al.  Pan-cancer integrative histology-genomic analysis via multimodal deep learning. , 2022, Cancer cell.

[9]  Ming Y. Lu,et al.  Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology , 2022, Medical Image Anal..

[10]  A. Kleppe.  Area under the curve may hide poor generalisation to external datasets , 2022, ESMO open.

[11]  Jakob Nikolas Kather,et al.  Artificial intelligence to identify genetic alterations in conventional histopathology , 2022, The Journal of pathology.

[12]  T. Brinker,et al.  Artificial intelligence for detection of microsatellite instability in colorectal cancer—a multicentric analysis of a pre-screening tool for clinical application , 2022, ESMO open.

[13]  Ming Y. Lu,et al.  Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies , 2022, Nature Medicine.

[14]  Jakob Nikolas Kather,et al.  Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology. , 2022, Journal of hepatology.

[15]  Jakob Nikolas Kather,et al.  DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathology , 2021, bioRxiv.

[16]  Jakob Nikolas Kather,et al.  The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups , 2021, bioRxiv.

[17]  Jakob Nikolas Kather,et al.  Deep Learning for the detection of microsatellite instability from histology images in colorectal cancer: a systematic literature review , 2021, ImmunoInformatics.

[18]  Jakob Nikolas Kather,et al.  Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer , 2021, The Journal of pathology.

[19]  N. Rajpoot,et al.  Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study , 2021, The Lancet. Digital health.

[20]  Jakob Nikolas Kather,et al.  Weakly supervised annotation‐free cancer detection and prediction of genotype in routine histopathology , 2021, The Journal of pathology.

[21]  Olivier Dehaene,et al.  Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers , 2021, COMPAY@MICCAI.

[22]  H. Horlings,et al.  DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer , 2021, Medical Image Anal..

[23]  Jakob Nikolas Kather,et al.  The impact of site-specific digital histology signatures on deep learning model accuracy and bias , 2021, Nature Communications.

[24]  H. Jang,et al.  Feasibility of deep learning‐based fully automated classification of microsatellite instability in tissue slides of colorectal cancer , 2021, International journal of cancer.

[25]  D. Kerr,et al.  Designing deep learning studies in cancer diagnostics , 2021, Nature Reviews Cancer.

[26]  N. Rajpoot,et al.  Novel deep learning algorithm predicts the status of molecular pathways and key mutations in colorectal cancer from routine histology images , 2021, medRxiv.

[27]  B. Martin,et al.  Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. , 2021, The Lancet. Oncology.

[28]  Anne L. Martel,et al.  Self supervised contrastive learning for digital histopathology , 2020, Machine Learning with Applications.

[29]  Jakob Nikolas Kather,et al.  Deep learning in cancer pathology: a new generation of clinical biomarkers , 2020, British Journal of Cancer.

[30]  H. Jang,et al.  Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning , 2020, World journal of gastroenterology.

[31]  Tang,et al.  Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer , 2020, Theranostics.

[32]  Jakob Nikolas Kather,et al.  Development of AI-based pathology biomarkers in gastrointestinal and liver cancer , 2020, Nature Reviews Gastroenterology & Hepatology.

[33]  Ming Y. Lu,et al.  AI-based pathology predicts origins for cancers of unknown primary , 2020, Nature.

[34]  Alexander T. Pearson,et al.  Clinical-grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning. , 2020, Gastroenterology.

[35]  Aly A. Khan,et al.  Learning relevant H&E slide morphologies for prediction of colorectal cancer tumor mutation burden using weakly supervised deep learning. , 2020 .

[36]  Jakob Nikolas Kather,et al.  The Aachen Protocol for Deep Learning Histopathology: A hands-on guide for data preprocessing , 2020 .

[37]  Geoffrey E. Hinton,et al.  A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.

[38]  Jeremy Howard,et al.  fastai: A Layered API for Deep Learning , 2020, Inf..

[39]  Jakob Nikolas Kather,et al.  Pan-cancer image-based detection of clinically actionable genetic alterations , 2019, Nature Cancer.

[40]  Alexander W. Jung,et al.  Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis , 2019, Nature Cancer.

[41]  A. Madabhushi,et al.  Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology , 2019, Nature Reviews Clinical Oncology.

[42]  Thomas J. Fuchs,et al.  Clinical-grade computational pathology using weakly supervised deep learning on whole slide images , 2019, Nature Medicine.

[43]  H. Grabsch,et al.  KRAS status is related to histological phenotype in gastric cancer: results from a large multicentre study , 2019, Gastric Cancer.

[44]  Jakob Nikolas Kather,et al.  Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer , 2019, Nature Medicine.

[45]  N. Razavian,et al.  Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning , 2018, Nature Medicine.

[46]  Max Welling,et al.  Attention-based Deep Multiple Instance Learning , 2018, ICML.

[47]  Nicholay Topin,et al.  Super-convergence: very fast training of neural networks using large learning rates , 2018, Defense + Commercial Sensing.

[48]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[49]  M. Kloor,et al.  No association of CpG island methylator phenotype and colorectal cancer survival: population-based study , 2016, British Journal of Cancer.

[50]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[51]  Z. Trajanoski,et al.  Integrative Analyses of Colorectal Cancer Show Immunoscore Is a Stronger Predictor of Patient Survival Than Microsatellite Instability. , 2016, Immunity.

[52]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[53]  M. Kloor,et al.  Statin use and survival after colorectal cancer: the importance of comprehensive confounder adjustment. , 2015, Journal of the National Cancer Institute.

[54]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[55]  Albert C. Koong,et al.  BRAF-mutated, Microsatellite-stable Adenocarcinoma of the Proximal Colon: An Aggressive Adenocarcinoma With Poor Survival, Mucinous Differentiation, and Adverse Morphologic Features , 2012, The American journal of surgical pathology.

[56]  D. Tait,et al.  Diagnosis and management of colorectal cancer: summary of NICE guidance , 2011, BMJ : British Medical Journal.

[57]  H. Brenner,et al.  Long-term risk of colorectal cancer after negative colonoscopy. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[58]  G. Bisi,et al.  Bone marrow disease detection with FDG-PET/CT and bone marrow biopsy during the staging of malignant lymphoma: results from a large multicentre study. , 2011, The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of....

[59]  D. Kerr,et al.  Value of mismatch repair, KRAS, and BRAF mutations in predicting recurrence and benefits from chemotherapy in colorectal cancer. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[60]  J. Mandrekar.  Receiver operating characteristic curve in diagnostic test assessment. , 2010, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[61]  J. S. Marron,et al.  A method for normalizing histology slides for quantitative analysis , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[62]  S. Gruber,et al.  Pathologic Predictors of Microsatellite Instability in Colorectal Cancer , 2009, The American journal of surgical pathology.

[63]  D. Kerr,et al.  Adjuvant chemotherapy versus observation in patients with colorectal cancer: a randomised study , 2007, The Lancet.

[64]  P. Quirke,et al.  Reporting colorectal cancer , 2007, Histopathology.

[65]  J Chang-Claude,et al.  Does a negative screening colonoscopy ever need to be repeated? , 2005, Gut.

[66]  M. Kloor,et al.  T25 repeat in the 3' untranslated region of the CASP2 gene: a sensitive and specific marker for microsatellite instability in colorectal cancer. , 2005, Cancer Research.

[67]  R. Penzel,et al.  Mutational activation of the RAS‐RAF‐MAPK and the wnt pathway in small intestinal adenocarcinomas , 2004, Scandinavian journal of gastroenterology.

[68]  Robert D Cardiff,et al.  Pathway pathology: histological differences between ErbB/Ras and Wnt pathway transgenic mammary tumors. , 2002, The American journal of pathology.

[69]  Jesús Chamorro-Martínez,et al.  Diatom autofocusing in brightfield microscopy: a comparative study , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[70]  S Srivastava,et al.  A National Cancer Institute Workshop on Microsatellite Instability for cancer detection and familial predisposition: development of international criteria for the determination of microsatellite instability in colorectal cancer. , 1998, Cancer research.

[71]  Zbigniew Leonowicz,et al.  Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network , 2022, IEEE Access.

[72]  Sue Belgrave,et al.  Guideline Development Group , 2014 .