Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging.

Journal: Journal of healthcare engineering
Published Date:

Abstract

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.

Authors

  • Gabriele Valvano
    IMT School for Advanced Studies Lucca, Lucca, Italy.
  • Gianmarco Santini
    Imaging Department, Fondazione Gabriele Monasterio, Massa, Italy.
  • Nicola Martini
    Imaging Department, Fondazione Gabriele Monasterio, Massa, Italy.
  • Andrea Ripoli
    Imaging Department, Fondazione Gabriele Monasterio, Massa, Italy.
  • Chiara Iacconi
    Azienda USL Toscana Nord Ovest (ATNO), Carrara, Italy.
  • Dante Chiappino
    Imaging Department, Fondazione Gabriele Monasterio, Massa, Italy.
  • Daniele Della Latta
    Imaging Department, Fondazione Gabriele Monasterio, Massa, Italy.