# Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging

arXiv: https://arxiv.org/abs/1809.03788 · HTML: https://mlsanity.com/paper/1809.03788/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [Segmenting Microcalcifications in Mammograms and its Applications](https://mlsanity.com/paper/2102.00811/) (2021)
- [Performance Analysis of UNet and Variants for Medical Image Segmentation](https://mlsanity.com/paper/2309.13013/) (2023)

## Abstract (arXiv)

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.