A Two-Stage Method for Skin Lesion Analysis

09/11/2018

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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.

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A Two-Stage Method for Skin Lesion Analysis

Automatic skin lesion segmentation with fully convolutional-deconvolutional networks

Skin Lesion Classification using Class Activation Map

Deeply Supervised Rotation Equivariant Network for Lesion Segmentation in Dermoscopy Images

Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs

Liver lesion segmentation informed by joint liver segmentation

A Multi-stage Framework with Context Information Fusion Structure for Skin Lesion Segmentation

An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis

A Two-Stage Method for Skin Lesion Analysis

Related Research

Automatic skin lesion segmentation with fully convolutional-deconvolutional networks

Skin Lesion Classification using Class Activation Map

Deeply Supervised Rotation Equivariant Network for Lesion Segmentation in Dermoscopy Images

Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs

Liver lesion segmentation informed by joint liver segmentation

A Multi-stage Framework with Context Information Fusion Structure for Skin Lesion Segmentation

An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis