Unsupversied feature correlation model to predict breast abnormal
  variation maps in longitudinal mammograms
Jun Bai Annie Jin Madison Adams Clifford Yang Sheida Nabavi
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.