7 papers · 1 filter
EXACT: an explainable anomaly-aware vision foundation model for analysis of 3D chest CT
Xuguang Bai, Mingxuan Liu, Tongxi Song +6
Chest computed tomography (CT) is central to the detection and management of thoracic disease, yet the growing scale and complexity of volumetric imaging increasingly exceed what c…
TiS-TSL: Image-Label Supervised Surgical Video Stereo Matching via Time-Switchable Teacher-Student Learning
Rui Wang, Ying Zhou, Hao Wang +3
Stereo matching in minimally invasive surgery (MIS) is essential for next-generation navigation and augmented reality. Yet, dense disparity supervision is nearly impossible due to…
Moving Light Adaptive Colonoscopy Reconstruction via Illumination-Attenuation-Aware 3D Gaussian Splatting
Hao Wang, Ying Zhou, Haoyu Zhao +5
3D Gaussian Splatting (3DGS) enables real-time view synthesis in colonoscopy but assumes static illumination, making it incompatible with the strong photometric variations caused b…
FreeVPS: Repurposing Training-Free SAM2 for Generalizable Video Polyp Segmentation
Qiang Hu, Ying Zhou, Gepeng Ji +3
Existing video polyp segmentation (VPS) paradigms usually struggle to balance between spatiotemporal modeling and domain generalization, limiting their applicability in real clinic…
SALI: Short-term Alignment and Long-term Interaction Network for Colonoscopy Video Polyp Segmentation
Qiang Hu, Zhenyu Yi, Ying Zhou +4
Colonoscopy videos provide richer information in polyp segmentation for rectal cancer diagnosis. However, the endoscope's fast moving and close-up observing make the current method…
MonoPCC: Photometric-invariant Cycle Constraint for Monocular Depth Estimation of Endoscopic Images
Zhiwei Wang, Ying Zhou, Shiquan He +6
Photometric constraint is indispensable for self-supervised monocular depth estimation. It involves warping a source image onto a target view using estimated depth&pose, and then m…
MonoBox: Tightness-free Box-supervised Polyp Segmentation using Monotonicity Constraint
Qiang Hu, Zhenyu Yi, Ying Zhou +4
We propose MonoBox, an innovative box-supervised segmentation method constrained by monotonicity to liberate its training from the user-unfriendly box-tightness assumption. In cont…