Summary of Adversarial-robust Transfer Learning For Medical Imaging Via Domain Assimilation, by Xiaohui Chen and Tie Luo
Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation
by Xiaohui Chen, Tie Luo
First submitted to arxiv on: 25 Feb 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 This paper explores the trustworthiness issues in AI-driven medical diagnosis, which relies on machine learning and deep learning models to analyze medical images. Despite their high accuracy, these models are vulnerable to manipulation by introducing subtle perturbations to the original image. The scarcity of publicly available medical images hinders reliable training, leading to reliance on transfer learning from natural images, which is prone to domain discrepancy and vulnerability to adversarial attacks. To address this issue, the authors propose a domain assimilation approach that adapts texture and color while preserving texture to suppress distortion. They analyze the performance of transfer learning under various adversarial attacks, demonstrating high effectiveness in reducing attack efficacy and contributing to more trustworthy transfer learning in biomedical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In medical imaging, AI helps doctors detect diseases from images. But these AI models are not always reliable because they can be tricked into making mistakes by adding small changes to the images. This is a problem because there aren’t many public images of medical conditions for training AI models. Instead, scientists use images of natural objects like animals and landscapes, which isn’t perfect because those images look very different from medical images. To make AI more reliable, researchers developed an approach that makes AI models better at understanding texture and color in medical images while avoiding distortion. The results show that this approach is effective in making AI less susceptible to tricks. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Transfer learning
