A Detection and Segmentation Architecture for Skin Lesion Segmentation on Dermoscopy Images

Abstract

This report summarises our method and validation results for the ISIC Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection - Task 1: Lesion Segmentation. We present a two-stage method for lesion segmentation with optimised training method and ensemble post-process. Our method achieves state-of-the-art performance on lesion segmentation and we win the first place in ISIC 2018 task1.

arXiv comment5 pages, 9 figures, Ranked 1st place in ISIC 2018 task1, title updated and results added

Cited by in corpus (4)

• A survey, review, and future trends of skin lesion segmentation and classification
• Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations
• Lesion Net -- Skin Lesion Segmentation Using Coordinate Convolution and Deep Residual Units
• TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors