Summary of The Impact Of An Xai-augmented Approach on Binary Classification with Scarce Data, by Ximing Wen et al.
The Impact of an XAI-Augmented Approach on Binary Classification with Scarce Data
by Ximing Wen, Rosina O. Weber, Anik Sen, Darryl Hannan, Steven C. Nesbit, Vincent Chan, Alberto Goffi, Michael Morris, John C. Hunninghake, Nicholas E. Villalobos, Edward Kim, Christopher J. MacLellan
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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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 paper addresses the challenge of developing accurate machine learning classifiers for Point-of-Care Ultrasound (POCUS) interpretation, a critical task in emergency situations where specialist training and experience may not be available. The authors hypothesize that traditional approaches to training classifiers with scarce data may lead to overfitting due to the limited number of positive training instances. To overcome this limitation, they propose an Explainable AI-Augmented approach that leverages human expertise to help the algorithm learn more from less, potentially improving generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The goal is to develop a machine learning model that can accurately interpret POCUS images even when specialist training and experience are not available. The challenge lies in obtaining positive training images, which is scarce and difficult to obtain. The proposed approach uses Explainable AI-Augmented methods to help the algorithm learn from limited data and improve generalization. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Overfitting
