Summary of Tta-ood: Test-time Augmentation For Improving Out-of-distribution Detection in Gastrointestinal Vision, by Sandesh Pokhrel et al.
TTA-OOD: Test-time Augmentation for Improving Out-of-Distribution Detection in Gastrointestinal Vision
by Sandesh Pokhrel, Sanjay Bhandari, Eduard Vazquez, Tryphon Lambrou, Prashnna Gyawali, Binod Bhattarai
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 research paper proposes a novel approach to detecting abnormal cases in endoscopic images of the gastrointestinal tract. The authors frame this problem as an out-of-distribution (OOD) detection task, where a model trained on in-distribution (ID) data can identify healthy cases while abnormalities are detected as OOD. To enhance the distinction between ID and OOD examples, the authors introduce test-time augmentation into the OOD detection pipeline. This approach improves the effectiveness of existing OOD methods with the same model by shifting the pixel space, resulting in a more distinct semantic representation for OOD examples compared to ID examples. The proposed method achieves state-of-the-art OOD scores and has potential applications in disease diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors use computers to better diagnose diseases from endoscopic images of the gut. Right now, it’s hard to find abnormal cases because there aren’t many examples of what normal looks like. To fix this, researchers treat finding abnormalities as a special kind of problem where they train a model on what normal looks like and then look for things that are different. They also add some extra steps to help the computer understand when something is truly unusual. This makes it better at spotting abnormal cases than other methods. |
