Summary of Towards Out-of-distribution Detection For Breast Cancer Classification in Point-of-care Ultrasound Imaging, by Jennie Karlsson et al.
Towards Out-of-Distribution Detection for breast cancer classification in Point-of-Care Ultrasound Imaging
by Jennie Karlsson, Marisa Wodrich, Niels Christian Overgaard, Freja Sahlin, Kristina Lång, Anders Heyden, Ida Arvidsson
First submitted to arxiv on: 29 Feb 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 Deep learning has significant potential in medical applications, where trustworthy algorithms are essential to ensure reliable assessments. Out-of-distribution (OOD) sample detection is critical for building a safe classifier. Following previous research on classifying breast cancer in point-of-care ultrasound images, this study investigates OOD detection using three methods: softmax, energy score, and deep ensembles. The methods are tested on three different OOD datasets. Results show that the energy score method outperforms the softmax method, excelling in two of the datasets. The ensemble method demonstrates robustness, performing best at detecting OOD samples across all three datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about using artificial intelligence to help doctors diagnose medical problems more accurately. One important step is to make sure the AI system doesn’t give wrong answers when it’s not supposed to. This can happen if the system sees something that’s not normal, like a weird image or data. The researchers tested three ways to do this: softmax, energy score, and deep ensembles. They used these methods on different kinds of abnormal data sets. The results showed that one method, energy score, did really well in two of the tests. The third method, deep ensemble, was the most reliable at detecting when something is not normal. |
Keywords
» Artificial intelligence » Deep learning » Softmax
