  Dermatological diagnosis automation is essential in addressing the high
prevalence of skin diseases and critical shortage of dermatologists. Despite
approaching expert-level diagnosis performance, convolutional neural network
(ConvNet) adoption in clinical practice is impeded by their limited
explainability, and by subjective, expensive explainability validations. We
introduce DermX and DermX+, an end-to-end framework for explainable automated
dermatological diagnosis. DermX is a clinically-inspired explainable
dermatological diagnosis ConvNet, trained using DermXDB, a 554 image dataset
annotated by eight dermatologists with diagnoses, supporting explanations, and
explanation attention maps. DermX+ extends DermX with guided attention training
for explanation attention maps. Both methods achieve near-expert diagnosis
performance, with DermX, DermX+, and dermatologist F1 scores of 0.79, 0.79, and
0.87, respectively. We assess the explanation performance in terms of
identification and localization by comparing model-selected with
dermatologist-selected explanations, and gradient-weighted class-activation
maps with dermatologist explanation maps, respectively. DermX obtained an
identification F1 score of 0.77, while DermX+ obtained 0.79. The localization
F1 score is 0.39 for DermX and 0.35 for DermX+. These results show that
explainability does not necessarily come at the expense of predictive power, as
our high-performance models provide expert-inspired explanations for their
diagnoses without lowering their diagnosis performance.
