2022 DermXAnEndtoEndFrameworkforExpl

(Jalaboi et al., 2022) ⇒ Raluca Jalaboi, Frederik Faye, Mauricio Orbes-Arteaga, Dan JÃ¸rgensen, Ole Winther, and Alfiia Galimzianova. (2022). “DermX: An End-to-end Framework for Explainable Automated Dermatological Diagnosis.” In: Medical Image Analysis Journal.

Subject Headings: Dermatological Diagnosis; Explainability; Image Dataset, Convolutional Neural Networks; DermX; Clinically-Inspired Model.

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http://scholar.google.com/scholar?q=%222022%22+DermX%3A+An+End-to-end+Framework+for+Explainable+Automated+Dermatological+Diagnosis

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Abstract

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, 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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2022 DermXAnEndtoEndFrameworkforExpl	Raluca Jalaboi Frederik Faye Mauricio Orbes-Arteaga Dan JÃ¸rgensen Ole Winther Alfiia Galimzianova			DermX: An End-to-end Framework for Explainable Automated Dermatological Diagnosis						2022