2.8k citations · 3k across the 4 of their papers we have counts for
4 papers
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…
Segment Anything in Medical Images and Videos: Benchmark and Deployment
Jun Ma, Sumin Kim, Feifei Li +4
Recent advances in segmentation foundation models have enabled accurate and efficient segmentation across a wide range of natural images and videos, but their utility to medical da…
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
Jun Ma, Feifei Li, Bo Wang
Convolutional Neural Networks (CNNs) and Transformers have been the most popular architectures for biomedical image segmentation, but both of them have limited ability to handle lo…
Segment Anything in Medical Images
Jun Ma, Yuting He, Feifei Li +3
Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, oft…