DeepHealth
Published August 22, 2019 | Version 1
Conference paper Open

Skin Lesion Segmentation Ensemble with Diverse Training Strategies

Creators

  • 1. Dipartimento di Ingegneria "Enzo Ferrari", Università degli Studi di Modena e Reggio Emilia, Modena, Italy

Description

This paper presents a novel strategy to perform skin lesion segmentation from dermoscopic images. We design an effective segmentation pipeline, and explore several pre-training methods to initialize the features extractor, highlighting how different procedures lead the Convolutional Neural Network (CNN) to focus on different features. An encoder-decoder segmentation CNN is employed to take advantage of each pre-trained features extractor. Experimental results reveal how multiple initialization strategies can be exploited, by means of an ensemble method, to obtain state-of-the-art skin lesion segmentation accuracy.

Files

2019_CAIP_REDUCED_Skin_Lesion_Segmentation_Ensemble_with_Diverse_Training_Strategies.pdf

Files (9.5 MB)

Additional details

Funding

DeepHealth – Deep-Learning and HPC to Boost Biomedical Applications for Health 825111
European Commission