Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association
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Andrew H. Beck | Liron Pantanowitz | Mark D Zarella | Famke Aeffner | Cleopatra Kozlowski | Anil V Parwani | Esther Abels | Jeroen Vd Laak | Marilyn M Bui | Venkata N P Vemuri | Jeff Gibbs | Emmanuel Agosto-Arroyo | Andrew H Beck | Venkata. N. P. Vemuri | A. Beck | L. Pantanowitz | A. Parwani | M. Bui | E. Abels | Cleopatra Kozlowski | M. Zarella | F. Aeffner | Emmanuel Agosto-Arroyo | Jeroen Vd Laak | Jeff D. Gibbs | Esther Abels
[1] Anant Madabhushi,et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent , 2017, Scientific Reports.
[2] Douglas Bowman,et al. Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association , 2019, Journal of pathology informatics.
[3] Rajarsi R. Gupta,et al. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. , 2018, Cell reports.
[4] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[5] Luca Maria Gambardella,et al. Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.
[6] Alexis B. Carter,et al. Computational Pathology: A Path Ahead. , 2016, Archives of pathology & laboratory medicine.
[7] M. Rantalainen,et al. Digital image analysis of Ki67 in hot spots is superior to both manual Ki67 and mitotic counts in breast cancer , 2018, Histopathology.
[8] Franco Turini,et al. A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..
[9] Brad Bolon,et al. Commentary: Roles for Pathologists in a High-throughput Image Analysis Team. , 2016, Toxicologic pathology.
[10] Usha Sinha,et al. A Review of Medical Imaging Informatics , 2002, Annals of the New York Academy of Sciences.
[11] Pascal Vincent,et al. Visualizing Higher-Layer Features of a Deep Network , 2009 .
[12] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[13] Bin Liu,et al. Crowdsourcing the General Public for Large Scale Molecular Pathology Studies in Cancer , 2015, EBioMedicine.
[14] Paolo Zaffino,et al. Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach , 2019, International Journal of Computer Assisted Radiology and Surgery.
[15] F. Sardanelli,et al. Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States , 2018, Insights into Imaging.
[16] Tahsin Kurc,et al. Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives , 2018, Journal of pathology informatics.
[17] Ron Kikinis,et al. Implementing the DICOM Standard for Digital Pathology , 2018, Journal of pathology informatics.
[18] Navid Farahani,et al. A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association. , 2018, Archives of pathology & laboratory medicine.
[19] Andrew H. Beck,et al. Computational Pathology to Discriminate Benign from Malignant Intraductal Proliferations of the Breast , 2014, PloS one.
[20] Andrew H. Beck,et al. Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd. , 2014, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.
[21] Yukako Yagi,et al. Color standardization and optimization in Whole Slide Imaging , 2011, Diagnostic pathology.
[22] Joel H. Saltz,et al. Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images , 2018, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.
[23] Jayashree Kalpathy-Cramer,et al. Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive. , 2014, Translational oncology.
[24] Stanley Cohen,et al. A perspective on digital and computational pathology , 2015, Journal of pathology informatics.
[25] Meyke Hermsen,et al. 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset , 2018, GigaScience.
[26] David G. Schwartz,et al. A Review and Assessment Framework for Mobile-Based Emergency Intervention Apps , 2018, ACM Comput. Surv..
[27] Toby C. Cornish,et al. US Food and Drug Administration Approval of Whole Slide Imaging for Primary Diagnosis: A Key Milestone Is Reached and New Questions Are Raised. , 2018, Archives of pathology & laboratory medicine.
[28] Philip R. O. Payne,et al. Questions for Artificial Intelligence in Health Care. , 2019, JAMA.
[29] Syed Muhammad Anwar,et al. Deep Learning in Medical Image Analysis , 2017 .
[30] Anne E Carpenter,et al. Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.
[31] Jelena Jovanovic,et al. Semantic annotation in biomedicine: the current landscape , 2017, Journal of Biomedical Semantics.
[32] S. Shuangshoti,et al. Comparison between digital image analysis and visual assessment of immunohistochemical HER2 expression in breast cancer. , 2018, Pathology, research and practice.
[33] Gad Getz,et al. Computational pathology: an emerging definition. , 2014, Archives of pathology & laboratory medicine.
[34] Bo Wang,et al. Weakly supervised mitosis detection in breast histopathology images using concentric loss , 2019, Medical Image Anal..
[35] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[36] F. N. L. Poynter,et al. Marcello Malpighi and the Evolution of Embryology , 1967, Medical History.
[37] Joachim M. Buhmann,et al. Computational Pathology: Challenges and Promises for Tissue Analysis , 2015, Comput. Medical Imaging Graph..
[38] Philippe Lambin,et al. Decision Support Systems in Oncology , 2019, JCO clinical cancer informatics.
[39] Andrew H. Beck,et al. Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival , 2011, Science Translational Medicine.
[40] Jill S Barnholtz-Sloan,et al. Predicting cancer outcomes from histology and genomics using convolutional networks , 2017 .
[41] Nico Karssemeijer,et al. Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks , 2018, IEEE Transactions on Medical Imaging.
[42] Andreas Uhl,et al. Do We Need Annotation Experts? A Case Study in Celiac Disease Classification , 2014, MICCAI.
[43] T. Hermanns,et al. Automated Gleason grading of prostate cancer tissue microarrays via deep learning , 2018, bioRxiv.
[44] Jonathan D. Beezley,et al. Structured crowdsourcing enables convolutional segmentation of histology images , 2019, Bioinform..
[45] David P Bauer,et al. Quanti.us: a tool for rapid, flexible, crowd-based annotation of images , 2018, Nature Methods.
[46] Joel H. Saltz,et al. Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Liron Pantanowitz,et al. Artificial Intelligence and Digital Pathology: Challenges and Opportunities , 2018, Journal of pathology informatics.
[48] F. N. L. Poynter. Marcello Malpighi and the Evolution of Embryology , by Howard B. Adelmann, 5 vols. (pp. 2475) 4to, 16 plates (11 col.), map, Cornell University Press and Oxford University Press, 1966. £70. , 1967 .
[49] Mark D. Zarella,et al. Laboratory Computer Performance in a Digital Pathology Environment: Outcomes from a Single Institution , 2018, Journal of pathology informatics.
[50] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[51] Andrew H. Beck,et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.
[52] 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.
[53] Mark D Zarella,et al. Estimation of Fine-Scale Histologic Features at Low Magnification. , 2018, Archives of pathology & laboratory medicine.
[54] Ramakrishnan Mukundan,et al. HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues , 2017, Histopathology.
[55] Alfred Winter,et al. Towards a Software Tool for Planning IHE-Compliant Information Systems , 2019, EFMI-STC.
[56] J A Beliën,et al. Origins of ... image analysis in clinical pathology. , 1997, Journal of clinical pathology.
[57] Jie Xu,et al. The practical implementation of artificial intelligence technologies in medicine , 2019, Nature Medicine.
[58] Maria S. Kulikova,et al. Mitosis detection in breast cancer histological images An ICPR 2012 contest , 2013, Journal of pathology informatics.
[59] Cris L. Luengo Hendriks,et al. The Gold Standard Paradox in Digital Image Analysis: Manual Versus Automated Scoring as Ground Truth. , 2017, Archives of pathology & laboratory medicine.
[60] George Lee,et al. Image analysis and machine learning in digital pathology: Challenges and opportunities , 2016, Medical Image Anal..
[61] J. Murphy. The General Data Protection Regulation (GDPR) , 2018, Irish medical journal.
[62] Mark D Zarella,et al. An alternative reference space for H&E color normalization , 2017, PloS one.