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09/13/2023
SAMUS: Adapting Segment Anything Model for Clinically-Friendly and Generalizable Ultrasound Image Segmentation
Segment anything model (SAM), an eminent universal image segmentation mo...
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09/09/2023
ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation
Transformers have been extensively studied in medical image segmentation...
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05/09/2023
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity
Federated noisy label learning (FNLL) is emerging as a promising tool fo...
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05/01/2023
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring
Limited training data and severe class imbalance impose significant chal...
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11/12/2022
Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation
Vessel segmentation is essential in many medical image applications, suc...
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06/29/2022
The Lighter The Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning
Vision transformers have recently set off a new wave in the field of med...
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06/29/2022
C2FTrans: Coarse-to-Fine Transformers for Medical Image Segmentation
Convolutional neural networks (CNN), the most prevailing architecture fo...
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06/28/2022
FedRare: Federated Learning with Intra- and Inter-Client Contrast for Effective Rare Disease Classification
Federated learning (FL), enabling different medical institutions or clie...
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05/04/2022
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation
The purpose of federated learning is to enable multiple clients to joint...
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11/17/2020