3 papers
cs.CV2026
DiffuSAM: Diffusion-Based Prompt-Free SAM2 for Few-Shot and Source-Free Medical Image Segmentation
Tal Grossman, Noa Cahan, Lev Ayzenberg +1
Segmentation models such as Segment Anything Model (SAM) and SAM2 achieve strong prompt-driven zero-shot performance. However, their training on natural images limits domain transf…
cs.CV2026
Anatomical Token Uncertainty for Transformer-Guided Active MRI Acquisition
Lev Ayzenberg, Shady Abu-Hussein, Raja Giryes +1
Full data acquisition in MRI is inherently slow, which limits clinical throughput and increases patient discomfort. Compressed Sensing MRI (CS-MRI) seeks to accelerate acquisition…
cs.CV2024
ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models
Lev Ayzenberg, Raja Giryes, Hayit Greenspan
This work introduces a new framework, ProtoSAM, for one-shot medical image segmentation. It combines the use of prototypical networks, known for few-shot segmentation, with SAM - a…