Explainable Transformer Prototypes for Medical Diagnoses

arXiv:2403.06961v1 Announce Type: new
Abstract: Deployments of artificial intelligence in medical diagnostics mandate not just accuracy and efficacy but also trust, emphasizing the need for explainability in machine decisions. The recent trend in automated medical image diagnostics leans towards the deployment of Transformer-based architectures, credited to their impressive capabilities. Since the self-attention feature of transformers contributes towards identifying crucial regions during the classification process, they enhance the trustability of the methods. However, the complex intricacies of these attention mechanisms may …

abstract accuracy architectures artificial artificial intelligence arxiv attention automated capabilities cs.cv decisions deployment deployments diagnostics explainability feature image intelligence machine medical self-attention transformer trend trust type