Unsupversied feature correlation model to predict breast abnormal variation maps in longitudinal mammograms

Abstract

Breast cancer continues to be a significant cause of mortality among women globally. Timely identification and precise diagnosis of breast abnormalities are critical for enhancing patient prognosis. In this study, we focus on improving the early detection and accurate diagnosis of breast abnormalities, which is crucial for improving patient outcomes and reducing the mortality rate of breast cancer. To address the limitations of traditional screening methods, a novel unsupervised feature correlation network was developed to predict maps indicating breast abnormal variations using longitudinal 2D mammograms. The proposed model utilizes the reconstruction process of current year and prior year mammograms to extract tissue from different areas and analyze the differences between them to identify abnormal variations that may indicate the presence of cancer. The model is equipped with a feature correlation module, an attention suppression gate, and a breast abnormality detection module that work together to improve the accuracy of the prediction. The proposed model not only provides breast abnormal variation maps, but also distinguishes between normal and cancer mammograms, making it more advanced compared to the state-of the-art baseline models. The results of the study show that the proposed model outperforms the baseline models in terms of Accuracy, Sensitivity, Specificity, Dice score, and cancer detection rate.