31 citations · 64 across the 20 of their papers we have counts for
5 papers · 1 filter
Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation
Tianyi Shi, Xiaohuan Ding, Wei Zhou +4
Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. However, achieving high pixel-w…
The Lighter The Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning
Xian Lin, Li Yu, Kwang-Ting Cheng +1
Vision transformers have recently set off a new wave in the field of medical image analysis due to their remarkable performance on various computer vision tasks. However, recent hy…
BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation
Xian Lin, Li Yu, Kwang-Ting Cheng +1
Objective: Transformers, born to remedy the inadequate receptive fields of CNNs, have drawn explosive attention recently. However, the daunting computational complexity of global r…
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification
Nannan Wu, Li Yu, Xin Yang +2
Federated learning (FL), training deep models from decentralized data without privacy leakage, has shown great potential in medical image computing recently. However, considering t…
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation
Jeffry Wicaksana, Zengqiang Yan, Dong Zhang +4
The purpose of federated learning is to enable multiple clients to jointly train a machine learning model without sharing data. However, the existing methods for training an image…