Deep learning from multiple experts improves identification of amyloid neuropathologies

[1]  Mariel G Kozberg,et al.  Deep learning assisted quantitative assessment of histopathological markers of Alzheimer’s disease and cerebral amyloid angiopathy , 2021, Acta Neuropathologica Communications.

[2]  Michael J. Keiser,et al.  Validation of machine learning models to detect amyloid pathologies across institutions , 2020, Acta Neuropathologica Communications.

[3]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[4]  C. Feudtner,et al.  Developing, Administering, and Scoring the Healthcare Ethics Consultant Certification Examination. , 2019, The Hastings Center report.

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

[6]  Sanjeev Arora,et al.  A Simple Saliency Method That Passes the Sanity Checks , 2019, ArXiv.

[7]  Jason Y. Park,et al.  Trends in the US and Canadian Pathologist Workforces From 2007 to 2017 , 2019, JAMA network open.

[8]  Phedias Diamandis,et al.  Physician perspectives on integration of artificial intelligence into diagnostic pathology , 2019, npj Digital Medicine.

[9]  Marcel Prastawa,et al.  Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy , 2019, Laboratory Investigation.

[10]  Q. Bassat,et al.  Collaborative intelligence and gamification for on-line malaria species differentiation , 2019, Malaria Journal.

[11]  Alex John London,et al.  Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. , 2019, The Hastings Center report.

[12]  Fei Wang,et al.  Deep Learning in Medicine-Promise, Progress, and Challenges. , 2019, JAMA internal medicine.

[13]  Michael J. Keiser,et al.  Comment on “Predicting reaction performance in C–N cross-coupling using machine learning” , 2018, Science.

[14]  Michael J. Keiser,et al.  Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline , 2018, bioRxiv.

[15]  Been Kim,et al.  Sanity Checks for Saliency Maps , 2018, NeurIPS.

[16]  Zachary C. Lipton,et al.  The mythos of model interpretability , 2018, Commun. ACM.

[17]  Michael Wainberg,et al.  Deep learning in biomedicine , 2018, Nature Biotechnology.

[18]  Kevin Smith,et al.  Digital image analysis in breast pathology-from image processing techniques to artificial intelligence. , 2017, Translational research : the journal of laboratory and clinical medicine.

[19]  Daisuke Komura,et al.  Machine Learning Methods for Histopathological Image Analysis , 2017, Computational and structural biotechnology journal.

[20]  Geoffrey E. Hinton,et al.  Who Said What: Modeling Individual Labelers Improves Classification , 2017, AAAI.

[21]  Heung-Il Suk,et al.  Deep Learning in Medical Image Analysis. , 2017, Annual review of biomedical engineering.

[22]  Ankur Taly,et al.  Axiomatic Attribution for Deep Networks , 2017, ICML.

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

[24]  Dennis W Dickson,et al.  Pathology of Neurodegenerative Diseases. , 2017, Cold Spring Harbor perspectives in biology.

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

[26]  Jason Lines,et al.  HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles for Time Series Classification , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

[27]  Dhruv Khullar,et al.  Reducing Diagnostic Errors--Why Now? , 2015, The New England journal of medicine.

[28]  Xiaogang Wang,et al.  Saliency detection by multi-context deep learning , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Michael Vitale,et al.  The wisdom of crowds , 2016, The Lancet.

[30]  K. Jellinger,et al.  Development, appraisal, validation and implementation of a consensus protocol for the assessment of cerebral amyloid angiopathy in post-mortem brain tissue. , 2014, American journal of neurodegenerative disease.

[31]  Andrew Zisserman,et al.  Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.

[32]  Subramanian Ramanathan,et al.  Learning from multiple annotators with varying expertise , 2013, Machine Learning.

[33]  M. Graber.  The incidence of diagnostic error in medicine , 2013, BMJ quality & safety.

[34]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[35]  M. McHugh.  Interrater reliability: the kappa statistic , 2012, Biochemia medica.

[36]  J. Schneider,et al.  National Institute on Aging–Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease , 2012, Alzheimer's & Dementia.

[37]  Charles Duyckaerts,et al.  National Institute on Aging–Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease: a practical approach , 2011, Acta Neuropathologica.

[38]  Gerardo Hermosillo,et al.  Learning From Crowds , 2010, J. Mach. Learn. Res..

[39]  A. Madabhushi,et al.  Histopathological Image Analysis: A Review , 2009, IEEE Reviews in Biomedical Engineering.

[40]  Gerardo Hermosillo,et al.  Supervised learning from multiple experts: whom to trust when everyone lies a bit , 2009, ICML '09.

[41]  Burr Settles,et al.  Active Learning Literature Survey , 2009 .

[42]  Gerald van Belle,et al.  Consortium to Establish a Registry for Alzheimer’s Disease (CERAD): The first twenty years , 2008, Alzheimer's & Dementia.

[43]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[44]  H. Braak,et al.  Two Types of Sporadic Cerebral Amyloid Angiopathy , 2002, Journal of neuropathology and experimental neurology.

[45]  P. Wesseling,et al.  Neuropathological diagnostic accuracy , 2002, British journal of neurosurgery.

[46]  Ronald Azuma,et al.  Recent Advances in Augmented Reality , 2001, IEEE Computer Graphics and Applications.

[47]  Erik Reinhard,et al.  Color Transfer between Images , 2001, IEEE Computer Graphics and Applications.

[48]  J. Vickers,et al.  The morphological phenotype of β-amyloid plaques and associated neuritic changes in Alzheimer’s disease , 2001, Neuroscience.

[49]  J. Vickers,et al.  The morphological phenotype of beta-amyloid plaques and associated neuritic changes in Alzheimer's disease. , 2001, Neuroscience.

[50]  Patrick R Hof,et al.  Practical approaches to stereology in the setting of aging- and disease-related brain banks , 2000, Journal of Chemical Neuroanatomy.

[51]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[52]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[53]  S. Greenberg,et al.  Diagnosis of cerebral amyloid angiopathy. Sensitivity and specificity of cortical biopsy. , 1997, Stroke.

[54]  D. Dickson,et al.  The Pathogenesis of Senile Plaques , 1997, Journal of neuropathology and experimental neurology.

[55]  G. Fuller,et al.  Diagnostic discrepancies and their clinical impact in a neuropathology referral practice , 1997, Cancer.

[56]  J. Diamond,et al.  Disagreement among health care professionals about the urgent care needs of emergency department patients. , 1996, Annals of emergency medicine.

[57]  B. Crain,et al.  Interlaboratory Comparison of Neuropathology Assessments in Alzheimer's Disease: A Study of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) , 1994, Journal of neuropathology and experimental neurology.

[58]  Pietro Perona,et al.  Inferring Ground Truth from Subjective Labelling of Venus Images , 1994, NIPS.

[59]  J. Vonsattel,et al.  Cerebral amyloid angiopathy without and with cerebral hemorrhages: A comparative histological study , 1991, Annals of neurology.

[60]  Robert C. Wolpert,et al.  A Review of the , 1985 .

[61]  A. P. Dawid,et al.  Maximum Likelihood Estimation of Observer Error‐Rates Using the EM Algorithm , 1979 .