1. Evaluation of a Deep Convolutional Neural Network method for the segmentation of breast microcalcifications in Mammography Imaging
- Author
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Nicola Martini, Andrea Gori, Gianmarco Santini, Andrea Ripoli, Daniele Della Latta, Chiara Iacconi, Gabriele Valvano, Luigi Landini, and Dante Chiappino
- Subjects
medicine.medical_specialty ,Computer science ,Biomedical Engineering ,Bioengineering ,Convolutional neural network ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Segmentation ,0302 clinical medicine ,Breast cancer ,medicine ,Mammography ,Mammography imaging ,Pixel ,Artificial neural network ,medicine.diagnostic_test ,business.industry ,Deep learning ,Pattern recognition ,Microcalcification ,medicine.disease ,Deep convolutional neural network ,Radiology ,Artificial intelligence ,medicine.symptom ,business ,030217 neurology & neurosurgery - Abstract
Cluster of microcalcifications can be an early sign of breast cancer. In this paper we present a deep convolutional neural network for microcalcification detection and compare its results to a classical approach. In this work we used 238 mammograms to train and validate our neural network to recognize which pixels in a mammogram correspond to a calcification; we tested the results on 52 images and obtained an accuracy of 83.7% against only 58% of the classical approach. Our results show how deep learning could be an effective tool to use for microcalcification detection and segmentation, outdoing classical approaches.
- Published
- 2017