Summary of Towards Case-based Interpretability For Medical Federated Learning, by Laura Latorre et al.
Towards Case-based Interpretability for Medical Federated Learning
by Laura Latorre, Liliana Petrychenko, Regina Beets-Tan, Taisiya Kopytova, Wilson Silva
First submitted to arxiv on: 24 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 explores the application of deep generative models in medical federated learning settings to generate case-based explanations for AI model decisions. The authors highlight the importance of interpretability in increasing trust and adoption of AI in clinical practice, particularly in light of shifting medical AI training paradigms towards federated learning due to data protection regulations. In a federated setting where past data is inaccessible, the authors propose using deep generative models to generate synthetic examples that protect privacy while explaining decisions. The proof-of-concept focuses on pleural effusion diagnosis using publicly available Chest X-ray data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors use AI in hospitals better by making it easier to understand how AI makes its decisions. Right now, AI is trained with lots of medical images, but that data is protected and can’t be shared between hospitals. To solve this problem, the authors develop a new way to generate fake medical images that are similar to real ones, which helps doctors understand why the AI made a certain decision. This is important because it increases trust in AI and makes it more useful in hospitals. |
Keywords
» Artificial intelligence » Federated learning
