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20232026
most citedNarrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation

4 citations · 5 across the 3 of their papers we have counts for

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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…