Summary of Taming the Tail: Leveraging Asymmetric Loss and Pade Approximation to Overcome Medical Image Long-tailed Class Imbalance, by Pankhi Kashyap et al.
Taming the Tail: Leveraging Asymmetric Loss and Pade Approximation to Overcome Medical Image Long-Tailed Class Imbalance
by Pankhi Kashyap, Pavni Tandon, Sunny Gupta, Abhishek Tiwari, Ritwik Kulkarni, Kshitij Sharad Jadhav
First submitted to arxiv on: 5 Oct 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 This paper addresses long-tailed problems in healthcare classification, which arise from imbalanced data due to varying medical condition prevalence. Traditional loss functions like cross-entropy and binary cross-entropy are insufficient for classifying under-represented conditions in medical image datasets. The authors introduce a novel polynomial loss function based on Pade approximation, designed to overcome long-tailed classification challenges. This approach incorporates asymmetric sampling techniques to better classify under-represented classes. Evaluations were conducted on four datasets (three public and one proprietary) using the proposed loss function, which is open-sourced in the ALPA repository. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps doctors by improving how computers identify medical images with different conditions. Sometimes there are more images of some conditions than others, making it hard for computers to learn from them. The authors created a new way for computers to understand these imbalanced image datasets. They tested this approach on several real-world datasets and made the code available online. |
Keywords
» Artificial intelligence » Classification » Cross entropy » Loss function
