Clinically inspired enhance Explainability and Interpretability of an AI-Tool for BCC diagnosis based on expert annotation
This work addresses the need for interpretable AI tools to assist dermatologists in early BCC detection and referral, though it is incremental as it builds on existing XAI methods like Grad-CAM.
The paper tackled the problem of improving explainability and interpretability in an AI tool for basal cell carcinoma (BCC) diagnosis via teledermatology, achieving a BCC/non-BCC classification accuracy of 90% and clinically-inspired pattern detection accuracy of 99%.
An AI tool has been developed to provide interpretable support for the diagnosis of BCC via teledermatology, thus speeding up referrals and optimizing resource utilization. The interpretability is provided in two ways: on the one hand, the main BCC dermoscopic patterns are found in the image to justify the BCC/Non BCC classification. Secondly, based on the common visual XAI Grad-CAM, a clinically inspired visual explanation is developed where the relevant features for diagnosis are located. Since there is no established ground truth for BCC dermoscopic features, a standard reference is inferred from the diagnosis of four dermatologists using an Expectation Maximization (EM) based algorithm. The results demonstrate significant improvements in classification accuracy and interpretability, positioning this approach as a valuable tool for early BCC detection and referral to dermatologists. The BCC/non-BCC classification achieved an accuracy rate of 90%. For Clinically-inspired XAI results, the detection of BCC patterns useful to clinicians reaches 99% accuracy. As for the Clinically-inspired Visual XAI results, the mean of the Grad-CAM normalized value within the manually segmented clinical features is 0.57, while outside this region it is 0.16. This indicates that the model struggles to accurately identify the regions of the BCC patterns. These results prove the ability of the AI tool to provide a useful explanation.