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.

翻译：乳腺癌仍然是全球女性死亡的重要原因之一。及时识别与精确诊断乳腺异常对于改善患者预后至关重要。本研究聚焦于提升乳腺异常的早期检测与准确诊断能力，这对改善患者预后、降低乳腺癌死亡率具有关键意义。为克服传统筛查方法的局限性，我们开发了一种新型无监督特征相关网络，利用纵向二维乳腺X光片预测指示乳腺异常变化的图谱。该模型通过重构当年及既往年度乳腺X光片，提取不同区域的组织特征，并分析其差异以识别可能提示癌症存在的异常变化。模型配备了特征相关模块、注意力抑制门及乳腺异常检测模块，三者协同提升预测精度。该模型不仅能提供乳腺异常变化图谱，还可区分正常与患癌的乳腺X光片，相较于现有最优基线模型更具先进性。研究结果表明，在准确率、灵敏度、特异度、Dice分数及癌症检出率等指标上，本模型均优于基线模型。