Interpretable Machine Learning for Oral Lesion Diagnosis through Prototypical Instances Identification
This work addresses the need for interpretable machine learning in healthcare to assist clinicians in oral lesion diagnosis, though it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of diagnosing oral lesions from images by applying an interpretable prototype selection model called PivotTree, which achieved competitive performance in detecting neoplastic, aphthous, and traumatic ulcerated lesions through comparisons with expert-selected prototypes.
Decision-making processes in healthcare can be highly complex and challenging. Machine Learning tools offer significant potential to assist in these processes. However, many current methodologies rely on complex models that are not easily interpretable by experts. This underscores the need to develop interpretable models that can provide meaningful support in clinical decision-making. When approaching such tasks, humans typically compare the situation at hand to a few key examples and representative cases imprinted in their memory. Using an approach which selects such exemplary cases and grounds its predictions on them could contribute to obtaining high-performing interpretable solutions to such problems. To this end, we evaluate PivotTree, an interpretable prototype selection model, on an oral lesion detection problem, specifically trying to detect the presence of neoplastic, aphthous and traumatic ulcerated lesions from oral cavity images. We demonstrate the efficacy of using such method in terms of performance and offer a qualitative and quantitative comparison between exemplary cases and ground-truth prototypes selected by experts.