The Impact of an XAI-Augmented Approach on Binary Classification with Scarce Data

TL;DR

This paper explores how an Explainable AI-augmented approach can improve the accuracy and generalization of binary classifiers trained on scarce ultrasound image data, aiding clinical decision-making.

Contribution

It introduces an XAI-augmented method to enhance classifier performance with limited training data in point-of-care ultrasound applications.

Findings

XAI augmentation improves classifier accuracy with scarce data

Enhanced generalization reduces overfitting in ultrasound image classification

Method shows promise for real-time clinical decision support

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.