Summary of Triaug: Out-of-distribution Detection For Imbalanced Breast Lesion in Ultrasound, by Yinyu Ye et al.
TriAug: Out-of-Distribution Detection for Imbalanced Breast Lesion in Ultrasound
by Yinyu Ye, Shijing Chen, Dong Ni, Ruobing Huang
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 novel framework for detecting out-of-distribution (OOD) breast ultrasound images, which is crucial for accurate diagnosis of breast lesions. Traditional models struggle with unseen classes in clinical reality, even when trained on substantial in-distribution data. The proposed framework, built upon a long-tailed OOD detection task, incorporates triplet state augmentation (TriAug) to improve ID classification accuracy while maintaining strong OOD detection performance. Additionally, the framework employs a balanced sphere loss to address class imbalance issues. Experimental results demonstrate that the model outperforms state-of-the-art OOD approaches in both ID classification (F1-score=42.12%) and OOD detection (AUROC=78.06%). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if doctors could accurately diagnose breast lesions using ultrasound images, even when they’ve never seen that type of lesion before. This is a big problem because different types of breast lesions have very different incidence rates. The authors of this paper came up with a new way to solve this issue by training a model to detect when an image doesn’t belong to any known category. They used special tricks like mixing up the images and focusing on one type of lesion at a time. When they tested their approach, it outperformed other methods for detecting unknown lesions. This could lead to more accurate diagnoses and better patient outcomes. |
Keywords
* Artificial intelligence * Classification * F1 score
