Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching
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Pheng-Ann Heng | Qi Dou | Quande Liu | Hongzheng Yang | Q. Dou | P. Heng | Quande Liu | Hongzhen Yang
[1] D. Rueckert,et al. Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study , 2021, npj Digital Medicine.
[2] Pheng-Ann Heng,et al. FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Bradford J. Wood,et al. Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan , 2020, Medical Image Analysis.
[4] Alejandro F. Frangi,et al. Federated Simulation for Medical Imaging , 2020, MICCAI.
[5] Colin B. Compas,et al. Federated Learning for Breast Density Classification: A Real-World Implementation , 2020, DART/DCL@MICCAI.
[6] Joseph E. Gonzalez,et al. Benchmarking Semi-supervised Federated Learning , 2020, ArXiv.
[7] Rickmer Braren,et al. Secure, privacy-preserving and federated machine learning in medical imaging , 2020, Nature Machine Intelligence.
[8] Dimitris N. Metaxas,et al. Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Sandesh Ghimire,et al. Semi-supervised Medical Image Classification with Global Latent Mixing , 2020, MICCAI.
[10] Lequan Yu,et al. Semi-Supervised Medical Image Classification With Relation-Driven Self-Ensembling Model , 2020, IEEE Transactions on Medical Imaging.
[11] Joseph G. Akar,et al. Aggregating multiple real-world data sources using a patient-centered health-data-sharing platform , 2020, npj Digital Medicine.
[12] Yuan Zhang,et al. FocalMix: Semi-Supervised Learning for 3D Medical Image Detection , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Micah J. Sheller,et al. The future of digital health with federated learning , 2020, npj Digital Medicine.
[14] Lequan Yu,et al. MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data , 2020, IEEE Transactions on Medical Imaging.
[15] J. Duncan,et al. Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results , 2020, Medical Image Anal..
[16] Pheng Ann Heng,et al. Unpaired Multi-Modal Segmentation via Knowledge Distillation , 2020, IEEE Transactions on Medical Imaging.
[17] Nicolas Papadakis,et al. GraphX $$^\mathbf{\small NET } -$$ -Chest X-Ray Classification Under Extreme Minimal Supervision , 2019, MICCAI.
[18] Daguang Xu,et al. Privacy-preserving Federated Brain Tumour Segmentation , 2019, MLMI@MICCAI.
[19] Nicolas Papadakis,et al. GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision , 2019, 1907.10085.
[20] Chi-Wing Fu,et al. Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation , 2019, MICCAI.
[21] Yiming Li,et al. Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model , 2019, IPMI.
[22] Paul M. Thompson,et al. Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data , 2018, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).
[23] Andreas Bender,et al. Understanding and predicting disease relationships through similarity fusion , 2018, Bioinform..
[24] Josien P. W. Pluim,et al. Not‐so‐supervised: A survey of semi‐supervised, multi‐instance, and transfer learning in medical image analysis , 2018, Medical Image Anal..
[25] Spyridon Bakas,et al. Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation , 2018, BrainLes@MICCAI.
[26] Harald Kittler,et al. Descriptor : The HAM 10000 dataset , a large collection of multi-source dermatoscopic images of common pigmented skin lesions , 2018 .
[27] Ben Glocker,et al. Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation , 2017, MICCAI.
[28] Muhammad Imran Razzak,et al. Deep Learning for Medical Image Processing: Overview, Challenges and Future , 2017, ArXiv.
[29] Harri Valpola,et al. Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.
[30] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Blaise Agüera y Arcas,et al. Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.
[32] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[33] Deendayal Dinakarpandian,et al. Finding disease similarity based on implicit semantic similarity , 2012, J. Biomed. Informatics.