Automatic Infectious Disease Classification Analysis with Concept Discovery
This work addresses the need for interpretability in medical image analysis to improve trust and diagnosis accuracy for infectious diseases, but it appears incremental as it builds on existing concept discovery methods.
The paper tackles the problem of understanding neural network predictions in medical image classification for infectious diseases like tuberculosis and monkeypox by proposing a concept discovery method. It evaluates existing approaches and introduces NMFx, a unified formulation for interpretability across unsupervised, weakly supervised, and supervised scenarios.
Automatic infectious disease classification from images can facilitate needed medical diagnoses. Such an approach can identify diseases, like tuberculosis, which remain under-diagnosed due to resource constraints and also novel and emerging diseases, like monkeypox, which clinicians have little experience or acumen in diagnosing. Avoiding missed or delayed diagnoses would prevent further transmission and improve clinical outcomes. In order to understand and trust neural network predictions, analysis of learned representations is necessary. In this work, we argue that automatic discovery of concepts, i.e., human interpretable attributes, allows for a deep understanding of learned information in medical image analysis tasks, generalizing beyond the training labels or protocols. We provide an overview of existing concept discovery approaches in medical image and computer vision communities, and evaluate representative methods on tuberculosis (TB) prediction and monkeypox prediction tasks. Finally, we propose NMFx, a general NMF formulation of interpretability by concept discovery that works in a unified way in unsupervised, weakly supervised, and supervised scenarios.