Summary of Are We Ready For Out-of-distribution Detection in Digital Pathology?, by Ji-hun Oh et al.
Are We Ready for Out-of-Distribution Detection in Digital Pathology?
by Ji-Hun Oh, Kianoush Falahkheirkhah, Rohit Bhargava
First submitted to arxiv on: 18 Jul 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 In this paper, researchers tackle a crucial but overlooked challenge in digital pathology: detecting semantic and covariate out-of-distribution examples. The machine learning community has made progress on out-of-distribution detection, but how well do these methods fare in digital pathology applications? To address this question, the authors conduct a benchmark study, comparing diverse detectors in single and multi-model settings, exploring advanced machine learning techniques like transfer learning and architecture choice. This research provides new insights and guidelines for future studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about detecting things that don’t belong in pictures of cells. It’s important because it can help doctors make better decisions when looking at these images. The researchers tested different ways to do this detection, using methods from the machine learning field. They compared these methods and found out what works best. This helps us understand how to improve our ability to detect things that don’t belong. |
Keywords
» Artificial intelligence » Machine learning » Transfer learning
