Recurrent attention U-Net for segmentation and quantification of breast arterial calcifications on synthesized 2D mammograms
Manar AlJabri, Manal Alghamdi, Fernando Collado-Mesa, Mohamed Abdel-Mottaleb
TL;DR
A new deep learning model helps detect and quantify breast arterial calcifications in mammograms, which could be linked to cardiovascular disease.
Contribution
A novel recurrent attention U-Net model is introduced for BAC segmentation and quantification in synthesized 2D mammograms.
Findings
The model achieved 99.8861% overall accuracy in BAC detection.
It outperformed related models in terms of sensitivity and F-1 score.
Abstract
Breast arterial calcifications (BAC) are a type of calcification commonly observed on mammograms and are generally considered benign and not associated with breast cancer. However, there is accumulating observational evidence of an association between BAC and cardiovascular disease, the leading cause of death in women. We present a deep learning method that could assist radiologists in detecting and quantifying BAC in synthesized 2D mammograms. We present a recurrent attention U-Net model consisting of encoder and decoder modules that include multiple blocks that each use a recurrent mechanism, a recurrent mechanism, and an attention module between them. The model also includes a skip connection between the encoder and the decoder, similar to a U-shaped network. The attention module was used to enhance the capture of long-range dependencies and enable the network to effectively classify…
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Taxonomy
TopicsAI in cancer detection · Breast Lesions and Carcinomas · Digital Radiography and Breast Imaging