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Dafed: Domain-Aware Federated Learning with Latent Distribution Alignment for Cross-Center Generalization
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Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
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Privacy-Preserving Federated Brain Tumour Segmentation
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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Federated Learning in Medical Imaging: Part I: Toward Multicentral Health Care Ecosystems
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A survey on deep learning in medical image analysis
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Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
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inferredcites →https://doi.org/10.1016/j.neuroimage.2019.03.026openalex-snapshot
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A guide to deep learning in healthcare
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Federated learning for predicting clinical outcomes in patients with COVID-19
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Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
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End-to-end privacy preserving deep learning on multi-institutional medical imaging
inferredcites →https://doi.org/10.1109/3dv.2016.79openalex-snapshot
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
inferredcites →https://doi.org/10.1109/acpr.2015.7486599openalex-snapshot
Very deep convolutional neural network based image classification using small training sample size
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Going deeper with convolutions
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Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.195openalex-snapshot
Xception: Deep Learning with Depthwise Separable Convolutions
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Densely Connected Convolutional Networks
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Domain Generalization by Solving Jigsaw Puzzles
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FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
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RSCFed: Random Sampling Consensus Federated Semi-supervised Learning
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On the Effectiveness of Partial Variance Reduction in Federated Learning with Heterogeneous Data
inferredcites →https://doi.org/10.1109/icip.2019.8802942openalex-snapshot
Hepatic Lesion Segmentation by Combining Plain and Contrast-Enhanced CT Images with Modality Weighted U-Net
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Federated Learning With Non-IID Data: A Survey
inferredcites →https://doi.org/10.1109/msp.2020.2975749openalex-snapshot
Federated Learning: Challenges, Methods, and Future Directions
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Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique
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UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
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Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
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Deep learning and artificial intelligence in radiology: Current applications and future directions
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One weird trick for parallelizing convolutional neural networks
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
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A Probabilistic U-Net for Segmentation of Ambiguous Images
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On the effectiveness of partial variance reduction in federated learning with heterogeneous data
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