2 citations · 2 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation
Lev Ayzenberg, Raja Giryes, Hayit Greenspan
Deep learning models have emerged as the cornerstone of medical image segmentation, but their efficacy hinges on the availability of extensive manually labeled datasets and their a…