Summary of Enhancing Ai Diagnostics: Autonomous Lesion Masking Via Semi-supervised Deep Learning, by Ting-ruen Wei et al.
Enhancing AI Diagnostics: Autonomous Lesion Masking via Semi-Supervised Deep Learning
by Ting-Ruen Wei, Michele Hell, Dang Bich Thuy Le, Aren Vierra, Ran Pang, Mahesh Patel, Young Kang, Yuling Yan
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed unsupervised domain adaptation method generates image masks to outline regions of interest (ROIs) for differentiating breast lesions in breast ultrasound (US) imaging. The semi-supervised learning approach uses a primitive model trained on a small public breast US dataset with true annotations, which is then refined iteratively to generate pseudo-masks for a private, unannotated breast US dataset. The method employs downstream classification outcomes as a benchmark to guide the updating of pseudo-masks in subsequent iterations. The results show that the classification precision is highly correlated with the completeness of generated ROIs, promoting explainability of the deep learning classification model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a way to automatically identify important parts of breast ultrasound images using artificial intelligence. Right now, it’s hard to find these areas because they’re not clearly marked. This new method uses some initial training data and then adapts to more unknown data without human help. It improves the accuracy of identifying these areas by paying attention to how well the AI is doing its job. |
Keywords
* Artificial intelligence * Attention * Classification * Deep learning * Domain adaptation * Precision * Semi supervised * Unsupervised
