4 papers
MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis
Junkai Liu, Ling Shao, Le Zhang
Self-supervised learning (SSL) and diffusion models have respectively advanced representation learning and generative modeling for high-dimensional 3D visual data, yet they are oft…
DSVM-UNet : Enhancing VM-UNet with Dual Self-distillation for Medical Image Segmentation
Renrong Shao, Dongyang Li, Dong Xia +4
Vision Mamba models have been extensively researched in various fields, which address the limitations of previous models by effectively managing long-range dependencies with a line…
Contrastive Graph Modeling for Cross-Domain Few-Shot Medical Image Segmentation
Yuntian Bo, Tao Zhou, Zechao Li +2
Cross-domain few-shot medical image segmentation (CD-FSMIS) offers a promising and data-efficient solution for medical applications where annotations are severely scarce and multim…
Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Data
Qi Chen, Xinze Zhou, Chen Liu +11
AI for tumor segmentation is limited by the lack of large, voxel-wise annotated datasets, which are hard to create and require medical experts. In our proprietary JHH dataset of 3,…