5 citations · 6 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★ 1 cited
A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention
Amarjeet Kumar, Hongxu Jiang, Muhammad Imran +7
Deep learning has become the de facto method for medical image segmentation, with 3D segmentation models excelling in capturing complex 3D structures and 2D models offering high co…
cs.CV2024★ 5 cited
SAM-Lightening: A Lightweight Segment Anything Model with Dilated Flash Attention to Achieve 30 times Acceleration
Yanfei Song, Bangzheng Pu, Peng Wang +4
Segment Anything Model (SAM) has garnered significant attention in segmentation tasks due to their zero-shot generalization ability. However, a broader application of SAMs to real-…