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.

Skin Lesion Segmentation Ensemble with Diverse Training Strategies / Canalini, Laura; Pollastri, Federico; Bolelli, Federico; Cancilla, Michele; Allegretti, Stefano; Grana, Costantino. - 11678:(2019), pp. 89-101. (Intervento presentato al convegno International Conference on Computer Analysis of Images and Patterns tenutosi a Salerno, Italy nel Sep 3-5) [10.1007/978-3-030-29888-3_8].

Skin Lesion Segmentation Ensemble with Diverse Training Strategies

Laura Canalini
;
Federico Pollastri
;
Federico Bolelli;Michele Cancilla;Stefano Allegretti;Costantino Grana
2019

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.
2019
22-ago-2019
International Conference on Computer Analysis of Images and Patterns
Salerno, Italy
Sep 3-5
11678
89
101
Canalini, Laura; Pollastri, Federico; Bolelli, Federico; Cancilla, Michele; Allegretti, Stefano; Grana, Costantino
Skin Lesion Segmentation Ensemble with Diverse Training Strategies / Canalini, Laura; Pollastri, Federico; Bolelli, Federico; Cancilla, Michele; Allegretti, Stefano; Grana, Costantino. - 11678:(2019), pp. 89-101. (Intervento presentato al convegno International Conference on Computer Analysis of Images and Patterns tenutosi a Salerno, Italy nel Sep 3-5) [10.1007/978-3-030-29888-3_8].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1178303
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