1 citations · 2 across the 4 of their papers we have counts for
4 papers
Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer
Xinyuan Shao, Yiqing Shen, Mathias Unberath
Segment Anything Models (SAMs) have gained increasing attention in medical image analysis due to their zero-shot generalization capability in segmenting objects of unseen classes a…
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…
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…