Skin Lesion Segmentation Ensemble with Diverse Training Strategies
Abstract: 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.
Citation:
Canalini, Laura; Pollastri, Federico; Bolelli, Federico; Cancilla, Michele; Allegretti, Stefano; Grana, Costantino "Skin Lesion Segmentation Ensemble with Diverse Training Strategies" Computer Analysis of Images and Patterns, vol. 11678, Salerno, Italy, pp. 89 -101 , Sep 3-5, 2019 DOI: 10.1007/978-3-030-29888-3_8
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- DOI: 10.1007/978-3-030-29888-3_8