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Skin Lesion Segmentation Ensemble with Diverse Training Strategies

  • Laura Canalini
  • Federico Pollastri
  • Federico BolelliEmail author
  • Michele Cancilla
  • Stefano Allegretti
  • Costantino Grana
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11678)

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.

Keywords

Deep learning Convolutional Neural Networks Transfer learning Skin lesion segmentation 

Notes

Acknowledgments

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825111, DeepHealth Project.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Laura Canalini
    • 1
  • Federico Pollastri
    • 1
  • Federico Bolelli
    • 1
    Email author
  • Michele Cancilla
    • 1
  • Stefano Allegretti
    • 1
  • Costantino Grana
    • 1
  1. 1.Dipartimento di Ingegneria “Enzo Ferrari”Università degli Studi di Modena e Reggio EmiliaModenaItaly

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