Summary of Logex: Improved Tail Detection Of Extremely Rare Histopathology Classes Via Guided Diffusion, by Maximilian Mueller and Matthias Hein
LoGex: Improved tail detection of extremely rare histopathology classes via guided diffusion
by Maximilian Mueller, Matthias Hein
First submitted to arxiv on: 2 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 The paper presents a novel approach to detecting rare medical conditions, which are crucial to diagnose but challenging due to limited data. The authors focus on detecting these rare conditions as out-of-distribution data rather than attempting to classify them directly. To achieve this, they employ low-rank adaptation (LoRA) and diffusion guidance to generate targeted synthetic data for the detection task. The proposed method is evaluated on a histopathological task with impressive results, improving OOD detection performance while maintaining classification accuracy on common conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about detecting rare medical conditions that are hard to diagnose because there’s not much information available. Instead of trying to figure out what these conditions are, the researchers focus on identifying them as being different from normal cases. To do this, they create fake data that can help with detection and test it on a specific type of cancer diagnosis task. The results show that their method is good at detecting rare conditions while still accurately diagnosing common ones. |
Keywords
» Artificial intelligence » Classification » Diffusion » Lora » Low rank adaptation » Synthetic data
