5 papers
CoDG-Net: Structure-Guided Style Diffusion and Collaborative Learning to Mitigate Catastrophic Forgetting in Medical Image Domain Generalization
Yucheng Song, Jincan Wang, Haokang Ding +3
Domain Generalization (DG) for medical image segmentation is both highly challenging and critically important. However, existing medical DG methods largely overlook the issue of Ca…
Exploring Boundary-Aware Spatial-Frequency Fusion for Camouflaged Object Detection
Song Yu, Yang Hu, Haokang Ding +2
Camouflaged Object Detection is challenging due to the high degree of similarity between camouflaged objects and their surrounding backgrounds. Current COD methods mainly rely on e…
FGML-DG: Feynman-Inspired Cognitive Science Paradigm for Cross-Domain Medical Image Segmentation
Yucheng Song, Chenxi Li, Haokang Ding +2
In medical image segmentation across multiple modalities (e.g., MRI, CT, etc.) and heterogeneous data sources (e.g., different hospitals and devices), Domain Generalization (DG) re…
Medical Report Generation: A Hierarchical Task Structure-Based Cross-Modal Causal Intervention Framework
Yucheng Song, Yifan Ge, Junhao Li +2
Medical Report Generation (MRG) is a key part of modern medical diagnostics, as it automatically generates reports from radiological images to reduce radiologists' burden. However,…
TreeFedDG: Alleviating Global Drift in Federated Domain Generalization for Medical Image Segmentation
Yucheng Song, Chenxi Li, Haokang Ding +2
In medical image segmentation tasks, Domain Generalization (DG) under the Federated Learning (FL) framework is crucial for addressing challenges related to privacy protection and d…