Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions

Abstract

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.

References in corpus (2)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Residual Learning for Image Recognition

Cited by in corpus (7)

• Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
• Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification
• Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images
• The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification
• Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis
• A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification
• Multiple Convolutional Neural Network for Skin Dermoscopic Image Classification