Summary of A Federated Learning-friendly Approach For Parameter-efficient Fine-tuning Of Sam in 3d Segmentation, by Mothilal Asokan et al.
A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation
by Mothilal Asokan, Joseph Geo Benjamin, Mohammad Yaqub, Karthik Nandakumar
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 The paper proposes a novel approach for adapting foundation models for medical image analysis using Federated Learning (FL) while minimizing communication costs. By combining Parameter-Efficient Fine-tuning (PEFT) with FL, the authors develop plug-and-play Low-Rank Adapters (LoRA) to adapt the Segment Anything Model (SAM) for 3D medical image segmentation. The paper critically analyzes the contribution of each granular component of SAM on finetuning performance and identifies specific layers that can be federated efficiently while producing on-par accuracy. Experimental results show that retaining most of the decoder in its original state during adaptation is beneficial, achieving a significant reduction in communication cost (48x) compared to full fine-tuning while maintaining high performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a way to make medical image analysis better using special kinds of artificial intelligence models called foundation models. These models are trained on lots of data, but they can be tricky to use for specific tasks like analyzing medical images. The problem is that medical data is usually private and hard to share, which makes it difficult to train the models. The solution proposed in this paper uses a technique called Federated Learning (FL) that allows multiple people or organizations to work together on training the model without sharing their own data. This approach reduces the amount of information that needs to be shared between people, making it more efficient and secure. The authors tested their method on several medical image datasets and found that it can reduce communication costs by 48 times while still producing accurate results. |
Keywords
» Artificial intelligence » Decoder » Federated learning » Fine tuning » Image segmentation » Lora » Parameter efficient » Sam
