230 citations · 241 across the 3 of their papers we have counts for
3 papers
eess.IV2024
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…
eess.IV2024★ 11 cited
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…
eess.IV2024★ 230 cited
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…