Interpretable Diagnosis of Face Acne via Complementation Learning of Evidence Localization and Severity Level Grading.

Acne seriously affects people’s daily lives. Several studies of automated acne diagnosis either lack reasonable interpretation to support the diagnosis or ignore evidence in the diagnosis process. In this paper, we propose an interpretable diagnosis framework for face acne. This framework uses complementation learning of evidence localization and severity level grading to boost both streams by sharing the features that support each other. Evidence localization learns to identify lesion areas for supporting the diagnosis stream as well as providing interpretation. Severity level grading learns to recognize the diagnosis result and also provides reference and rectification for evidence localization. Experimental results show that complementation learning improves both evidence localization and severity level grading, the lesion areas from evidence localization can support the diagnosis and provide interpretations, and the diagnosis framework reaches the state-of-the-art level and the diagnostic performance of dermatologists.