Summary of Ordinal Multiple-instance Learning For Ulcerative Colitis Severity Estimation with Selective Aggregated Transformer, by Kaito Shiku et al.
Ordinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformer
by Kaito Shiku, Kazuya Nishimura, Daiki Suehiro, Kiyohito Tanaka, Ryoma Bise
First submitted to arxiv on: 22 Nov 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 This paper proposes a patient-level severity estimation method for ulcerative colitis (UC) diagnosis, addressing the limitations of previous image-level estimation methods. The proposed transformer-based approach utilizes selective aggregator tokens to aggregate features from multiple images taken from a patient, mimicking real clinical settings. This allows for more accurate estimation of severity scores and improved discriminative ability between adjacent classes. Experiments demonstrate the effectiveness of the method on two datasets, outperforming state-of-the-art multiple-instance learning (MIL) methods. The proposed approach is also evaluated in real clinical settings, showing superior performance compared to previous image-level methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors diagnose a condition called ulcerative colitis better. Right now, doctors usually look at just one picture of the patient’s colon to decide how severe their symptoms are. But what if they had multiple pictures taken from different parts of the colon? This would give them a more complete view and help them make a more accurate diagnosis. The researchers in this paper came up with an innovative way to use these multiple images to estimate the severity of the patient’s symptoms. They tested their method on two groups of patients and found that it worked better than other methods used before. |
Keywords
» Artificial intelligence » Transformer
