Summary of Medsaga: Few-shot Memory Efficient Medical Image Segmentation Using Gradient Low-rank Projection in Sam, by Navyansh Mahla et al.
MedSAGa: Few-shot Memory Efficient Medical Image Segmentation using Gradient Low-Rank Projection in SAM
by Navyansh Mahla, Annie D’souza, Shubh Gupta, Bhavik Kanekar, Kshitij Sharad Jadhav
First submitted to arxiv on: 21 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach, Medical Segment Anything Model with Galore MedSAGa, that enables memory-efficient and few-shot medical image segmentation using large-scale models. The proposed method combines the Segment Anything Model (SAM) with Gradient Low-Rank Projection GaLore to reduce computational requirements while maintaining performance. The model’s parameters are fine-tuned using standard optimizers, and its few-shot learning capabilities are evaluated across multiple standard medical image segmentation datasets. Compared to baseline models, including LoRA fine-tuned SAM (SAMed) and DAE-Former, MedSAGa demonstrates significant memory efficiency, achieving an average of 66% more memory savings than current state-of-the-art models. This makes MedSAGa a suitable solution for deployment in resource-constrained settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how to make large-scale medical image segmentation models work better and use less computer power. The new method, called Medical Segment Anything Model with Galore (MedSAGa), is designed to be more efficient and only needs a small number of examples to learn from. MedSAGa uses a special way of processing images and reduces the amount of memory needed, making it suitable for use in places where computer resources are limited. |
Keywords
» Artificial intelligence » Few shot » Image segmentation » Lora » Sam
