The Impact of an XAI-Augmented Approach on Binary Classification with Scarce Data
Proceedings of the XAI 2024 Workshop @ IJCAI
2024
Abstract
Point-of-Care Ultrasound (POCUS) is the practice of clinicians conducting and interpreting ultrasound scans right at the patient's bedside. However, the expertise needed to interpret these images is considerable and may not always be present in emergency situations. This reality makes algorithms such as machine learning classifiers extremely valuable to augment human decisions. POCUS devices are becoming available at a reasonable cost in the size of a mobile phone. The challenge of turning POCUS devices into life-saving tools is that interpretation of ultrasound images requires specialist training and experience. Unfortunately, the difficulty to obtain positive training images represents an important obstacle to building efficient and accurate classifiers. Hence, the problem we try to investigate is how to explore strategies to increase accuracy of classifiers trained with scarce data. We hypothesize that training with a few data instances may not suffice for classifiers to generalize causing them to overfit. Our approach uses an Explainable AI-Augmented approach to help the algorithm learn more from less and potentially help the classifier better generalize.
BibTeX
@inproceedings{wen-xai-2024,
title = {The Impact of an XAI-Augmented Approach on Binary Classification with Scarce Data},
author = {Wen, Ximing and Weber, Rosina O. and Sen, Anik and Hannan, Darryl and Nesbit, Steven C. and Chan, Vincent and Goffi, Alberto and Morris, Michael J. and Hunninghake, John C. and Villalobos, Nicholas E. and Kim, Edward and MacLellan, Christopher J.},
booktitle = {Proceedings of the XAI 2024 Workshop @ IJCAI},
year = {2024},
}