7 citations · 13 across the 26 of their papers we have counts for
6 papers · 1 filter
Operating Room Workflow Analysis via Reasoning Segmentation over Digital Twins
Yiqing Shen, Chenjia Li, Bohan Liu +3
Analyzing operating room (OR) workflows to derive quantitative insights into OR efficiency is important for hospitals to maximize patient care and financial sustainability. Prior w…
Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation
Yiqing Shen, Hao Ding, Xinyuan Shao +1
Fully supervised deep learning (DL) models for surgical video segmentation have been shown to struggle with non-adversarial, real-world corruptions of image quality including smoke…
Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIs
Yiqing Shen, Guannan He, Mathias Unberath
Brain tumor analysis in Magnetic Resonance Imaging (MRI) is crucial for accurate diagnosis and treatment planning. However, the task remains challenging due to the complexity and v…
FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification
Yiqing Shen, Xinyuan Shao, Blanca Inigo Romillo +2
Accurate segmentation of anatomical structures and pathological regions in medical images is crucial for diagnosis, treatment planning, and disease monitoring. While the Segment An…
MoE: A Foundation Model for Medical Multimodal Image Segmentation with Mixture of Experts
Yufeng Jiang, Yiqing Shen
Medical imaging data is inherently heterogeneous across different modalities and clinical centers, posing unique challenges for developing generalizable foundation models. Conventi…
FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images
Yiqing Shen, Jingxing Li, Xinyuan Shao +4
Segment anything models (SAMs) are gaining attention for their zero-shot generalization capability in segmenting objects of unseen classes and in unseen domains when properly promp…