Summary of Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?, by Kerem Cekmeceli et al.
Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?
by Kerem Cekmeceli, Meva Himmetoglu, Guney I. Tombak, Anna Susmelj, Ertunc Erdil, Ender Konukoglu
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Neural networks excel in supervised learning tasks when training and test data distributions align. However, their performance plummets under domain shift, a common issue in medical image segmentation due to varying scanner settings. Recently, foundational models (FMs) trained on large datasets have gained attention for their ability to be adapted for downstream tasks and achieve state-of-the-art performance with excellent generalization capabilities on natural images. This paper investigates the domain generalization performance of various FMs, including DinoV2, SAM, MedSAM, and MAE, when fine-tuned using parameter-efficient fine-tuning (PEFT) techniques such as Ladder and Rein (+LoRA) and decoder heads. The study introduces a novel decode head architecture, HQHSAM, which integrates elements from HSAM and HQSAM to enhance segmentation performance. Extensive experiments on multiple datasets reveal that FMs, particularly with the HQHSAM decode head, improve domain generalization for medical image segmentation. Additionally, PEFT technique effectiveness varies across different FMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how artificial intelligence can help doctors analyze medical images better. Medical images are taken using different machines and settings, which makes it hard for computers to understand them all the same way. Researchers have been working on a new type of computer model that can learn from lots of data and then apply what it learned to other tasks. This paper tests how well these models work when applied to medical image analysis. The results show that these models can be very helpful in analyzing medical images, especially when combined with special techniques. This is important because doctors need accurate information to make good decisions about patient care. |
Keywords
» Artificial intelligence » Attention » Decoder » Domain generalization » Fine tuning » Generalization » Image segmentation » Lora » Mae » Parameter efficient » Sam » Supervised
