4 citations · 7 across the 9 of their papers we have counts for
4 papers · 1 filter
MaskMed: Decoupled Mask and Class Prediction for Medical Image Segmentation
Bin Xie, Gady Agam
Medical image segmentation typically adopts a point-wise convolutional segmentation head to predict dense labels, where each output channel is heuristically tied to a specific clas…
RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2
Bin Xie, Hao Tang, Yan Yan +1
Segment Anything Model 2 (SAM 2), a prompt-driven foundation model extending SAM to both image and video domains, has shown superior zero-shot performance compared to its predecess…
Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation
Bin Xie, Hao Tang, Dawen Cai +2
Segment Anything Model (SAM) has demonstrated impressive zero-shot performance and brought a range of unexplored capabilities to natural image segmentation tasks. However, as a ver…
MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentation
Bin Xie, Hao Tang, Bin Duan +3
Segment Anything Model (SAM), a prompt-driven foundation model for natural image segmentation, has demonstrated impressive zero-shot performance. However, SAM does not work when di…