7 papers · 1 filter
Learning Where and What to Lift for Bi-planar X-ray-to-CT Reconstruction
Yifei Wu, Yicheng Wu, Qiang Ma +5
X-ray imaging can be approximately modeled as the projection of an underlying volumetric attenuation field, with each measurement recording the accumulated attenuation along a corr…
PAINT: Pathology-Aware Integrated Next-Scale Transformation for Virtual Immunohistochemistry
Rongze Ma, Mengkang Lu, Zhenyu Xiang +4
Virtual immunohistochemistry (IHC) aims to computationally synthesize molecular staining patterns from routine Hematoxylin and Eosin (H\&E) images, offering a cost-effective and ti…
Meta Curvature-Aware Minimization for Domain Generalization
Ziyang Chen, Yiwen Ye, Feilong Tang +2
Domain generalization (DG) aims to enhance the ability of models trained on source domains to generalize effectively to unseen domains. Recently, Sharpness-Aware Minimization (SAM)…
Day-Night Adaptation: An Innovative Source-free Adaptation Framework for Medical Image Segmentation
Ziyang Chen, Yiwen Ye, Yongsheng Pan +3
Distribution shifts widely exist in medical images acquired from different medical centres, hindering the deployment of semantic segmentation models trained on one centre (source d…
Gradient Alignment Improves Test-Time Adaptation for Medical Image Segmentation
Ziyang Chen, Yiwen Ye, Yongsheng Pan +1
Although recent years have witnessed significant advancements in medical image segmentation, the pervasive issue of domain shift among medical images from diverse centres hinders t…
MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer
Geng Chen, Junqing Yang, Xiaozhou Pu +5
Accurate segmentation of polyps from colonoscopy videos is of great significance to polyp treatment and early prevention of colorectal cancer. However, it is challenging due to the…
Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation
Ziyang Chen, Yongsheng Pan, Yiwen Ye +2
Distribution shift widely exists in medical images acquired from different medical centres and poses a significant obstacle to deploying the pre-trained semantic segmentation model…