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arXiv · 1809.03788

Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging

Gabriele Valvano·Gianmarco Santini·Nicola Martini·Andrea Ripoli·Chiara Iacconi·Dante Chiappino·Daniele Della Latta
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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 98.22% in the detection of preliminary suspect regions and of 97.47% in the segmentation task. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.

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Gabriele Valvano, Gianmarco Santini, Nicola Martini, Andrea Ripoli, Chiara Iacconi, Dante Chiappino, Daniele Della Latta. 2018-09-11. Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging. https://arxiv.org/abs/1809.03788

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