Collaborative training of medical artificial intelligence models with non-uniform labels
暂无分享,去创建一个
Jakob Nikolas Kather | Soroosh Tayebi Arasteh | S. Nebelung | D. Truhn | P. Isfort | F. Khader | G. Mueller-Franzes | C. Kuhl | Marwin Saehn | M. Saehn | Gustav Mueller-Franzes
[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 .