6 citations · 15 across the 8 of their papers we have counts for
7 papers
MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning
Dongnan Liu, Mariano Cabezas, Dongang Wang +16
Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data. Despite great success, FL's…
Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond
Tiange Xiang, Chaoyi Zhang, Xinyi Wang +4
U-Net, as an encoder-decoder architecture with forward skip connections, has achieved promising results in various medical image analysis tasks. Many recent approaches have also ex…
Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement
Dongnan Liu, Chaoyi Zhang, Yang Song +4
Recent advances in unsupervised domain adaptation (UDA) techniques have witnessed great success in cross-domain computer vision tasks, enhancing the generalization ability of data-…
BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation
Xinyi Wang, Tiange Xiang, Chaoyi Zhang +4
The recurrent mechanism has recently been introduced into U-Net in various medical image segmentation tasks. Existing studies have focused on promoting network recursion via reusin…
BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture
Tiange Xiang, Chaoyi Zhang, Dongnan Liu +3
U-Net has become one of the state-of-the-art deep learning-based approaches for modern computer vision tasks such as semantic segmentation, super resolution, image denoising, and i…
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting
Dongnan Liu, Donghao Zhang, Yang Song +5
Unsupervised domain adaptation (UDA) for nuclei instance segmentation is important for digital pathology, as it alleviates the burden of labor-intensive annotation and domain shift…