Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging
This work addresses early breast cancer detection for radiologists, but it is incremental as it applies existing deep learning methods to a specific medical imaging task.
The paper tackled the problem of detecting and segmenting microcalcification clusters in mammography imaging, achieving 98.22% accuracy in detection and 97.47% in segmentation using a dataset of 283 mammograms.
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.