502 citations · 507 across the 11 of their papers we have counts for
5 papers · 1 filter
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Jun Ma, Feifei Li, Sumin Kim +79
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive co…
Explainable and Controllable Motion Curve Guided Cardiac Ultrasound Video Generation
Junxuan Yu, Rusi Chen, Yongsong Zhou +7
Echocardiography video is a primary modality for diagnosing heart diseases, but the limited data poses challenges for both clinical teaching and machine learning training. Recently…
Robust Box Prompt based SAM for Medical Image Segmentation
Yuhao Huang, Xin Yang, Han Zhou +4
The Segment Anything Model (SAM) can achieve satisfactory segmentation performance under high-quality box prompts. However, SAM's robustness is compromised by the decline in box qu…
Mitral Regurgitation Recognition based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification
Zhe Liu, Xiliang Zhu, Tong Han +7
Mitral regurgitation (MR) is a serious heart valve disease. Early and accurate diagnosis of MR via ultrasound video is critical for timely clinical decision-making and surgical int…
UniUSNet: A Promptable Framework for Universal Ultrasound Disease Prediction and Tissue Segmentation
Zehui Lin, Zhuoneng Zhang, Xindi Hu +5
Ultrasound is widely used in clinical practice due to its affordability, portability, and safety. However, current AI research often overlooks combined disease prediction and tissu…