Explainable Transformer Prototypes for Medical Diagnoses
Ugur Demir Debesh Jha Zheyuan Zhang Elif Keles Bradley Allen Aggelos K. Katsaggelos Ulas Bagci
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
fall short of effectively pinpointing the regions of interest directly
influencing AI decisions. Our research endeavors to innovate a unique attention
block that underscores the correlation between 'regions' rather than 'pixels'.
To address this challenge, we introduce an innovative system grounded in
prototype learning, featuring an advanced self-attention mechanism that goes
beyond conventional ad-hoc visual explanation techniques by offering
comprehensible visual insights. A combined quantitative and qualitative
methodological approach was used to demonstrate the effectiveness of the
proposed method on the large-scale NIH chest X-ray dataset. Experimental
results showed that our proposed method offers a promising direction for
explainability, which can lead to the development of more trustable systems,
which can facilitate easier and rapid adoption of such technology into routine
clinics. The code is available at www.github.com/NUBagcilab/r2r_proto.