1 citations · 4 across the 19 of their papers we have counts for
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2D Versus 3D Diffusion for In Silico Training of Interventional X-ray AI Models
Sampath Rapuri, Jeremy Ko, Benjamin D. Killeen +2
The ability to synthesize realistic X-ray images has catalyzed the development of AI models for X-ray image-guided procedures, which otherwise suffer from a lack of available annot…
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…
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…
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…