Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions
This work addresses skin lesion diagnosis for medical applications, but it is incremental as it builds on existing CNN frameworks with domain-specific adaptations.
The authors tackled the problem of automatic diagnosis of skin lesions (nevus, melanoma, seborrheic keratosis) by incorporating dermatologists' expert knowledge into convolutional neural networks, achieving participation in the ISIC 2017 Challenge with a system that integrates lesion area identification, segmentation into structural patterns, and final diagnosis.
This report describes our submission to the ISIC 2017 Challenge in Skin Lesion Analysis Towards Melanoma Detection. We have participated in the Part 3: Lesion Classification with a system for automatic diagnosis of nevus, melanoma and seborrheic keratosis. Our approach aims to incorporate the expert knowledge of dermatologists into the well known framework of Convolutional Neural Networks (CNN), which have shown impressive performance in many visual recognition tasks. In particular, we have designed several networks providing lesion area identification, lesion segmentation into structural patterns and final diagnosis of clinical cases. Furthermore, novel blocks for CNNs have been designed to integrate this information with the diagnosis processing pipeline.