What is it about?
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.
Featured Image
Why is it important?
Microcalcification (MC) can be an indicator for the diagnosis of breast cancer as it is the expression of cell necrosis. Thus, their detection may be important for early assessment of cancer risk.
Perspectives
The Breast Imaging Reporting and Data System (BIRADS) standardized the interpretation of MCs by defining a scale ranging from 2 (benign finding) to 5 (highly suspicious of malignancy) based on their shape, density, and distribution within the breast. Subsequent to the detection of calcification clusters, further development of this work might deal with the identification of potential cancers or identification of BAC for the CVD stratification.
Gabriele Valvano
IMT Institute for Advanced Studies
Read the Original
This page is a summary of: Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging, Journal of Healthcare Engineering, April 2019, Hindawi Publishing Corporation,
DOI: 10.1155/2019/9360941.
You can read the full text:
Resources
Contributors
The following have contributed to this page
