Artificial Intelligence and Pathology: from Principles to Practice and Future Applications in Histomorphology and Molecular Profiling.
暂无分享,去创建一个
K. Müller | F. Tacke | M. Alber | P. Schirmacher | A. Stenzinger | D. Kazdal | J. Budczies | F. Klauschen | J. Lennerz | D. Capper | M. Bockmayr | M. Allgäuer | P. Jurmeister | Johannes Eschrich | A. Wagner | Daniel Kazdal
[1] B. van Ginneken,et al. Streaming Convolutional Neural Networks for End-to-End Learning With Multi-Megapixel Images , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] A. Stenzinger,et al. Strength in numbers: predicting response to checkpoint inhibitors from large clinical datasets , 2021, Cell.
[3] Francesco Ciompi,et al. Neural Image Compression for Gigapixel Histopathology Image Analysis , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Jakob Nikolas Kather,et al. Deep learning in cancer pathology: a new generation of clinical biomarkers , 2020, British Journal of Cancer.
[5] E. Cuppen,et al. Study protocol: Whole genome sequencing Implementation in standard Diagnostics for Every cancer patient (WIDE) , 2020, BMC medical genomics.
[6] T. Winter. Malicious Adversarial Attacks on Medical Image Analysis. , 2020, AJR. American journal of roentgenology.
[7] N. Kim,et al. Identification of potential causal variants for premature ovarian failure by whole exome sequencing , 2020, BMC medical genomics.
[8] N. Forkert,et al. Machine Learning for Precision Medicine. , 2020, Genome.
[9] Anmol Arora,et al. Pathology training in the age of artificial intelligence , 2020, Journal of Clinical Pathology.
[10] Howard Y. Chang,et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases , 2020, Nature Genetics.
[11] A. Phillippy,et al. Strategic vision for improving human health at The Forefront of Genomics , 2020, Nature.
[12] A. Shuaib,et al. The Increasing Role of Artificial Intelligence in Health Care: Will Robots Replace Doctors in the Future? , 2020, International journal of general medicine.
[13] Jon D. McAuliffe,et al. Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts , 2020, BMC Medical Genomics.
[14] Bertalan Meskó,et al. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database , 2020, npj Digital Medicine.
[15] E. Cuppen,et al. 1189O Validation of whole genome sequencing in routine clinical practice , 2020 .
[16] Suchi Saria,et al. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist , 2020, Nature Medicine.
[17] Andrew H. Beck,et al. Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancer , 2020, bioRxiv.
[18] Pierre Courtiol,et al. A deep learning model to predict RNA-Seq expression of tumours from whole slide images , 2020, Nature Communications.
[19] Mark Robson,et al. Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: A report from the ESMO Precision Medicine Working Group. , 2020, Annals of oncology : official journal of the European Society for Medical Oncology.
[20] L. Füzesi,et al. Number of pathologists in Germany: comparison with European countries, USA, and Canada , 2020, Virchows Archiv.
[21] Alex H. Wagner,et al. Collaborative, Multidisciplinary Evaluation of Cancer Variants Through Virtual Molecular Tumor Boards Informs Local Clinical Practices , 2020, JCO clinical cancer informatics.
[22] A. Goldenberg,et al. Machine learning approaches to drug response prediction: challenges and recent progress. , 2020, NPJ precision oncology.
[23] A. Khosla,et al. Machine learning-based identification of predictive features of the tumor micro-environment and vasculature in NSCLC patients using the IMpower150 study. , 2020 .
[24] Roderic Guigó,et al. PyHIST: A Histological Image Segmentation Tool , 2020, bioRxiv.
[25] D. Ruderman,et al. Deep learned tissue “fingerprints” classify breast cancers by ER/PR/Her2 status from H&E images , 2020, Scientific Reports.
[26] H. McAneney,et al. A scoping review and proposed workflow for multi-omic rare disease research , 2020, Orphanet Journal of Rare Diseases.
[27] S. Gerke,et al. Ethical and legal challenges of artificial intelligence-driven healthcare , 2020, Artificial Intelligence in Healthcare.
[28] Shuhao Wang,et al. Emerging role of deep learning‐based artificial intelligence in tumor pathology , 2020, Cancer communications.
[29] Alex H. Wagner,et al. A harmonized meta-knowledgebase of clinical interpretations of somatic genomic variants in cancer , 2020, Nature Genetics.
[30] A. Stenzinger,et al. Evaluation of a hybrid capture-based pan-cancer panel for analysis of treatment stratifying oncogenic aberrations and processes. , 2020, The Journal of molecular diagnostics : JMD.
[31] Wojciech Samek,et al. Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond , 2020, ArXiv.
[32] S. Fröhling,et al. Harmonization and Standardization of Panel-Based Tumor Mutational Burden (TMB) Measurement: Real-World Results and Recommendations of the QuIP Study. , 2020, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.
[33] Jeremy L. Warner,et al. A Review of Precision Oncology Knowledgebases for Determining the Clinical Actionability of Genetic Variants , 2020, Frontiers in Cell and Developmental Biology.
[34] M. Niranjan,et al. Robust subspace methods for outlier detection in genomic data circumvents the curse of dimensionality , 2020, Royal Society Open Science.
[35] David Capper,et al. DNA-Methylation-based Classification of Paediatric Brain Tumours. , 2020, Neuropathology and applied neurobiology.
[36] David T. W. Jones,et al. Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data , 2020, Nature Protocols.
[37] B. van Ginneken,et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. , 2020, The Lancet. Oncology.
[38] Jakob Nikolas Kather,et al. Pan-cancer image-based detection of clinically actionable genetic alterations , 2019, Nature Cancer.
[39] Alexander W. Jung,et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis , 2019, Nature Cancer.
[40] Klaus-Robert Müller,et al. Resolving challenges in deep learning-based analyses of histopathological images using explanation methods , 2019, Scientific Reports.
[41] Alexander Binder,et al. Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images , 2020, AI and ML for Digital Pathology.
[42] Theodoros Evgeniou,et al. Algorithms on regulatory lockdown in medicine , 2019, Science.
[43] Alexander V Penson,et al. Development of Genome-Derived Tumor Type Prediction to Inform Clinical Cancer Care. , 2019, JAMA oncology.
[44] George P Patrinos,et al. Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics. , 2019, Omics : a journal of integrative biology.
[45] Shradha Mukherjee. Genomics-Guided Immunotherapy for Precision Medicine in Cancer. , 2019, Cancer biotherapy & radiopharmaceuticals.
[46] Helen Pitman,et al. Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice , 2019, The Journal of pathology.
[47] Klaus-Robert Müller,et al. Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases , 2019, Science Translational Medicine.
[48] D. Heim,et al. Histomorphological and molecular profiling: friends not foes! Morpho‐molecular analysis reveals agreement between histological and molecular profiling , 2019, Histopathology.
[49] A. Madabhushi,et al. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology , 2019, Nature Reviews Clinical Oncology.
[50] Stephen S. F. Yip,et al. Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images , 2019, Journal of pathology informatics.
[51] Thomas J. Fuchs,et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images , 2019, Nature Medicine.
[52] L. Ivashkiv,et al. Interferon target-gene expression and epigenomic signatures in health and disease , 2019, Nature Immunology.
[53] Anand S Dighe,et al. Machine Learning and Other Emerging Decision Support Tools. , 2019, Clinics in laboratory medicine.
[54] Fabrice Andre,et al. Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women With Early-Stage Invasive Breast Cancer: ASCO Clinical Practice Guideline Update-Integration of Results From TAILORx. , 2019, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[55] Christopher Y. Park,et al. Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk , 2019, Nature Genetics.
[56] Phedias Diamandis,et al. Physician perspectives on integration of artificial intelligence into diagnostic pathology , 2019, npj Digital Medicine.
[57] Jakob Nikolas Kather,et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer , 2019, Nature Medicine.
[58] C. von Kalle,et al. Defective homologous recombination DNA repair as therapeutic target in advanced chordoma , 2019, Nature Communications.
[59] K. Müller,et al. Unmasking Clever Hans predictors and assessing what machines really learn , 2019, Nature Communications.
[60] Geert J. S. Litjens,et al. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology , 2019, Medical Image Anal..
[61] C. Denkert,et al. DNA methylation profiling reliably distinguishes pulmonary enteric adenocarcinoma from metastatic colorectal cancer , 2019, Modern Pathology.
[62] Shaoqun Zeng,et al. From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge , 2019, IEEE Transactions on Medical Imaging.
[63] P. Denéfle,et al. The contribution of genomics in the medicine of tomorrow, clinical applications and issues. , 2019, Therapie.
[64] Lon Phan,et al. SPDI: Data Model for Variants and Applications at NCBI , 2019, bioRxiv.
[65] T A Chan,et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.
[66] Inioluwa Deborah Raji,et al. Model Cards for Model Reporting , 2018, FAT.
[67] Klaus-Robert Müller,et al. iNNvestigate neural networks! , 2018, J. Mach. Learn. Res..
[68] D. Erhan,et al. A Benchmark for Interpretability Methods in Deep Neural Networks , 2018, NeurIPS.
[69] David F. Steiner,et al. Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer , 2018, The American journal of surgical pathology.
[70] Been Kim,et al. Sanity Checks for Saliency Maps , 2018, NeurIPS.
[71] Sander Canisius,et al. Molecular characterization of breast and lung tumors by integration of multiple data types with functional sparse-factor analysis , 2018, PLoS Comput. Biol..
[72] K-R Müller,et al. Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning. , 2018, Seminars in cancer biology.
[73] N. Razavian,et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning , 2018, Nature Medicine.
[74] E. King,et al. Pan-cancer deconvolution of tumour composition using DNA methylation , 2018, Nature Communications.
[75] Martin Sill,et al. Practical implementation of DNA methylation and copy-number-based CNS tumor diagnostics: the Heidelberg experience , 2018, Acta Neuropathologica.
[76] A. Jeyasekharan,et al. Biomarkers for Homologous Recombination Deficiency in Cancer , 2018, Journal of the National Cancer Institute.
[77] David T. W. Jones,et al. Primary intracranial spindle cell sarcoma with rhabdomyosarcoma-like features share a highly distinct methylation profile and DICER1 mutations , 2018, Acta Neuropathologica.
[78] Masaru Ishii,et al. Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles , 2018, ArXiv.
[79] Yifan Peng,et al. LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC , 2018, Nucleic Acids Res..
[80] Jochen K. Lennerz,et al. Artificial Intelligence Approach for Variant Reporting , 2018, JCO clinical cancer informatics.
[81] Till Acker,et al. DNA methylation-based classification of central nervous system tumours , 2018, Nature.
[82] T. Hermanns,et al. Automated Gleason grading of prostate cancer tissue microarrays via deep learning , 2018, Scientific Reports.
[83] I. Shmulevich,et al. Epigenetic profiling for the molecular classification of metastatic brain tumors , 2018, bioRxiv.
[84] J. Cuzick,et al. Comparison of the Performance of 6 Prognostic Signatures for Estrogen Receptor–Positive Breast Cancer , 2018, JAMA oncology.
[85] Max Welling,et al. Attention-based Deep Multiple Instance Learning , 2018, ICML.
[86] Wojciech Samek,et al. Methods for interpreting and understanding deep neural networks , 2017, Digit. Signal Process..
[87] Klaus-Robert Müller,et al. Learning how to explain neural networks: PatternNet and PatternAttribution , 2017, ICLR.
[88] P. Fasching,et al. Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy. , 2018, The Lancet. Oncology.
[89] H. Brenner,et al. Genome-wide DNA methylation analysis reveals a prognostic classifier for non-metastatic colorectal cancer (ProMCol classifier) , 2017, Gut.
[90] L. Harris,et al. Gene Expression Assays for Early-Stage Hormone Receptor–Positive Breast Cancer: Understanding the Differences , 2017, JNCI cancer spectrum.
[91] C. von Kalle,et al. Precision oncology based on omics data: The NCT Heidelberg experience , 2017, International journal of cancer.
[92] Z. Herceg,et al. Prognostic Classifier Based on Genome-Wide DNA Methylation Profiling in Well-Differentiated Thyroid Tumors , 2017, The Journal of clinical endocrinology and metabolism.
[93] Gianluca Bontempi,et al. DNA methylation–based immune response signature improves patient diagnosis in multiple cancers , 2017, The Journal of clinical investigation.
[94] Roland Eils,et al. The whole-genome landscape of medulloblastoma subtypes , 2017, Nature.
[95] C. Chakraborty,et al. An Advanced Deep Learning Approach for Ki-67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer , 2017, Scientific Reports.
[96] Martin Wattenberg,et al. SmoothGrad: removing noise by adding noise , 2017, ArXiv.
[97] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[98] Martin Sill,et al. DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis. , 2017, The Lancet. Oncology.
[99] Avanti Shrikumar,et al. Learning Important Features Through Propagating Activation Differences , 2017, ICML.
[100] Michel E. Vandenberghe,et al. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer , 2017, Scientific Reports.
[101] Jianhui Chen,et al. Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features , 2017, Neurocomputing.
[102] Ankur Taly,et al. Axiomatic Attribution for Deep Networks , 2017, ICML.
[103] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[104] Max Welling,et al. Visualizing Deep Neural Network Decisions: Prediction Difference Analysis , 2017, ICLR.
[105] Sebastian Thrun,et al. Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.
[106] Jean-Philippe Fortin,et al. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi , 2016, bioRxiv.
[107] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[108] K. Müller,et al. Explaining nonlinear classification decisions with deep Taylor decomposition , 2015, Pattern Recognit..
[109] Alexander Binder,et al. Evaluating the Visualization of What a Deep Neural Network Has Learned , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[110] P. Starostik. Clinical mutation assay of tumors: new developments , 2017, Anti-cancer drugs.
[111] R. Tothill,et al. Epigenetic profiling to classify cancer of unknown primary: a multicentre, retrospective analysis. , 2016, The Lancet. Oncology.
[112] L. V. van't Veer,et al. 70-Gene Signature as an Aid to Treatment Decisions in Early-Stage Breast Cancer. , 2016, The New England journal of medicine.
[113] Laxmi Parida,et al. Watson for Genomics: Moving Personalized Medicine Forward. , 2016, Trends in cancer.
[114] Rachel G Liao,et al. A federated ecosystem for sharing genomic, clinical data , 2016, Science.
[115] Raymond Dalgleish,et al. HGVS Recommendations for the Description of Sequence Variants: 2016 Update , 2016, Human mutation.
[116] Roland Eils,et al. Atypical Teratoid/Rhabdoid Tumors Are Comprised of Three Epigenetic Subgroups with Distinct Enhancer Landscapes. , 2016, Cancer cell.
[117] Roland Eils,et al. New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs , 2016, Cell.
[118] M. Gerstein,et al. Quantification of private information leakage from phenotype-genotype data: linking attacks , 2016, Nature Methods.
[119] M. Esteller,et al. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences , 2015, Epigenomics.
[120] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[121] Alexander Binder,et al. Analyzing Classifiers: Fisher Vectors and Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[122] W. Weichert,et al. Next-generation sequencing: hype and hope for development of personalized radiation therapy? , 2015, Radiation oncology.
[123] Alexander Binder,et al. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation , 2015, PloS one.
[124] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[125] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[126] Gary D Bader,et al. Molecular Classification of Ependymal Tumors across All CNS Compartments, Histopathological Grades, and Age Groups. , 2015, Cancer cell.
[127] Alla Alexandrovna Kornienko,et al. Knowledge in Artificial Intelligence Systems: Searching the Strategies for Application☆ , 2015 .
[128] Thomas Brox,et al. Striving for Simplicity: The All Convolutional Net , 2014, ICLR.
[129] Luca Maria Gambardella,et al. Assessment of algorithms for mitosis detection in breast cancer histopathology images , 2014, Medical Image Anal..
[130] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[131] Carsten Denkert,et al. Standardized Ki67 Diagnostics Using Automated Scoring—Clinical Validation in the GeparTrio Breast Cancer Study , 2014, Clinical Cancer Research.
[132] Serena Nik-Zainal,et al. Mechanisms underlying mutational signatures in human cancers , 2014, Nature Reviews Genetics.
[133] E. Li,et al. DNA methylation in mammals. , 2014, Cold Spring Harbor perspectives in biology.
[134] David T. W. Jones,et al. Signatures of mutational processes in human cancer , 2013, Nature.
[135] Jack Cuzick,et al. Comparison of PAM50 risk of recurrence score with oncotype DX and IHC4 for predicting risk of distant recurrence after endocrine therapy. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[136] L. Shaffer,et al. Cytogenetic Nomenclature: Changes in the ISCN 2013 Compared to the 2009 Edition , 2013, Cytogenetic and Genome Research.
[137] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[138] F. Markowetz,et al. Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling , 2012, Science Translational Medicine.
[139] Daniel Heim,et al. Detection and Segmentation of Cell Nuclei in Virtual Microscopy Images: A Minimum-Model Approach , 2012, Scientific Reports.
[140] Thomas Lengauer,et al. A DNA methylation fingerprint of 1628 human samples. , 2011, Genome research.
[141] R. Greil,et al. A New Molecular Predictor of Distant Recurrence in ER-Positive, HER2-Negative Breast Cancer Adds Independent Information to Conventional Clinical Risk Factors , 2011, Clinical Cancer Research.
[142] Motoaki Kawanabe,et al. How to Explain Individual Classification Decisions , 2009, J. Mach. Learn. Res..
[143] A. Nobel,et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[144] M. Cronin,et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. , 2004, The New England journal of medicine.
[145] Yudong D. He,et al. A Gene-Expression Signature as a Predictor of Survival in Breast Cancer , 2002 .
[146] R. Tibshirani,et al. Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[147] Yudong D. He,et al. Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.
[148] R. Tibshirani,et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[149] Christian A. Rees,et al. Molecular portraits of human breast tumours , 2000, Nature.
[150] D. Botstein,et al. Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.