Collaborative training of medical artificial intelligence models with non-uniform labels

[1]  Jakob Nikolas Kather,et al.  Artificial Intelligence for Clinical Interpretation of Bedside Chest Radiographs. , 2022, Radiology.

[2]  Jakob Nikolas Kather,et al.  Image prediction of disease progression for osteoarthritis by style-based manifold extrapolation , 2022, Nature Machine Intelligence.

[3]  M. Aloqaily,et al.  Federated learning review: Fundamentals, enabling technologies, and future applications , 2022, Inf. Process. Manag..

[4]  Jakob Nikolas Kather,et al.  Encrypted federated learning for secure decentralized collaboration in cancer image analysis , 2022, medRxiv.

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

[6]  P. Rajpurkar,et al.  AI in health and medicine , 2022, Nature Medicine.

[7]  Jakob Nikolas Kather,et al.  Swarm learning for decentralized artificial intelligence in cancer histopathology , 2021, Nature Medicine.

[8]  Kashif Ahmad,et al.  Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge , 2021, IEEE Open Journal of the Computer Society.

[9]  Binh T. Nguyen,et al.  VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations , 2020, Scientific Data.

[10]  D. Tao,et al.  A Survey on Vision Transformer , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  U. Raghavendra,et al.  Transfer learning techniques for medical image analysis: A review , 2021, Biocybernetics and Biomedical Engineering.

[12]  S. Shah,et al.  Harnessing multimodal data integration to advance precision oncology , 2021, Nature Reviews Cancer.

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

[14]  C. Leslie,et al.  Artificial intelligence in cancer research, diagnosis and therapy , 2021, Nature Reviews Cancer.

[15]  Colin B. Compas,et al.  Federated learning for predicting clinical outcomes in patients with COVID-19 , 2021, Nature Medicine.

[16]  Victor Ikechukwu A,et al.  ResNet-50 vs VGG-19 vs Training from Scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest x-ray images , 2021, Global Transitions Proceedings.

[17]  Catie Chang,et al.  Compound Figure Separation of Biomedical Images with Side Loss , 2021, DGM4MICCAI/DALI@MICCAI.

[18]  Yuankai Huo,et al.  VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning , 2021, MLMI@MICCAI.

[19]  Daniel Rueckert,et al.  End-to-end privacy preserving deep learning on multi-institutional medical imaging , 2021, Nature Machine Intelligence.

[20]  Neel S. Madhukar,et al.  Artificial Intelligence in Cancer Research and Precision Medicine. , 2021, Cancer discovery.

[21]  Mengling Feng,et al.  Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets. , 2021, Quantitative imaging in medicine and surgery.

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

[23]  Bingbing Ni,et al.  MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis , 2020, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI).

[24]  S. Gelly,et al.  An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2020, ICLR.

[25]  Mohammad Mohammadi,et al.  Medical imaging and computational image analysis in COVID-19 diagnosis: A review , 2020, Computers in Biology and Medicine.

[26]  Richard Nock,et al.  Advances and Open Problems in Federated Learning , 2019, Found. Trends Mach. Learn..

[27]  Jonathan Passerat-Palmbach,et al.  PySyft: A Library for Easy Federated Learning , 2021 .

[28]  Carlee Joe-Wong,et al.  Towards Flexible Device Participation in Federated Learning for Non-IID Data , 2020, AISTATS.

[29]  Stephen Lin,et al.  Swin Transformer: Hierarchical Vision Transformer using Shifted Windows , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[30]  Bo Jin,et al.  Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis , 2020, IEEE Access.

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

[32]  Spyridon Bakas,et al.  Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data , 2020, Scientific Reports.

[33]  Rickmer Braren,et al.  Secure, privacy-preserving and federated machine learning in medical imaging , 2020, Nature Machine Intelligence.

[34]  David Killock AI outperforms radiologists in mammographic screening , 2020, Nature Reviews Clinical Oncology.

[35]  David S. Melnick,et al.  International evaluation of an AI system for breast cancer screening , 2020, Nature.

[36]  Peter B. Walker,et al.  Federated Learning for Healthcare Informatics , 2019, Journal of Healthcare Informatics Research.

[37]  Steven Horng,et al.  MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports , 2019, Scientific Data.

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

[39]  Roger G. Mark,et al.  MIMIC-CXR: A large publicly available database of labeled chest radiographs , 2019, ArXiv.

[40]  Yifan Yu,et al.  CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison , 2019, AAAI.

[41]  Constantino Carlos Reyes-Aldasoro,et al.  Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study , 2019, PLoS medicine.

[42]  Ronald M. Summers,et al.  NegBio: a high-performance tool for negation and uncertainty detection in radiology reports , 2017, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

[43]  Muktabh Mayank Srivastava,et al.  Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs , 2017, ICIAR.

[44]  Ilker Ünal,et al.  Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach , 2017, Comput. Math. Methods Medicine.

[45]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[46]  Blaise Agüera y Arcas,et al.  Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.

[47]  Peter Richtárik,et al.  Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.

[48]  Peter Richtárik,et al.  Federated Optimization: Distributed Machine Learning for On-Device Intelligence , 2016, ArXiv.

[49]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[50]  Frank Konietschke,et al.  Bootstrapping and permuting paired t-test type statistics , 2014, Stat. Comput..

[51]  Stephen Wright,et al.  An alternative approach. , 2010, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

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