4 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.CV2026
Visually-Guided Controllable Medical Image Generation via Fine-Grained Semantic Disentanglement
Xin Huang, Junjie Liang, Qingshan Hou +4
Medical image synthesis is crucial for alleviating data scarcity and privacy constraints. However, fine-tuning general text-to-image (T2I) models remains challenging, mainly due to…
cs.CV2024★ 1 cited
Rethinking Barely-Supervised Volumetric Medical Image Segmentation from an Unsupervised Domain Adaptation Perspective
Zhiqiang Shen, Peng Cao, Junming Su +2
This paper investigates an extremely challenging problem: barely-supervised volumetric medical image segmentation (BSS). A BSS training dataset consists of two parts: 1) a barely-a…
eess.IV2023★ 4 cited
Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation
Haonan Wang, Peng Cao, Xiaoli Liu +2
Most state-of-the-art methods for medical image segmentation adopt the encoder-decoder architecture. However, this U-shaped framework still has limitations in capturing the non-loc…