激素受体（HR）在分子水平上的状态对于乳腺癌的准确诊断和有效治疗至关重要。与此同时，乳腺X线摄影是一种有效的乳腺癌筛查方法，可显著提高患者的生存率。然而，诊断乳腺癌分子状态需要进行病理活检，这可能会影响诊断的准确性。为了非侵入性诊断乳腺癌的激素受体（HR）状态并减少手动注释，我们提出了一种弱监督的深度学习框架BSNet，用于检测带有激素受体状态和良性肿瘤的乳腺癌。BSNet模型在哈尔滨医科大学癌症医院于2017年至2018年期间进行数字乳腺X线摄影的2321例女性多视图乳腺摄影病例上进行了训练，并在外部队列上进行了验证。BSNet在测试集和外部验证集上的平均AUC分别为0.89和0.92。BSNet在非侵入性乳腺癌诊断中具有优异性能，可使用多视图乳腺摄影图像而无需像素注释。此外，我们还开发了一个易于使用的网页服务器（http://bsnet.edbc.org）。BSNet描述了乳腺癌亚型的高维乳腺X线摄影图像，有助于早期管理方案的制定。© 2023 The Authors.

The status of hormone receptors (HR) at the molecular level is crucial for accurate diagnosis and effective treatment of breast cancer. Meanwhile, mammography is an effective screening method for detecting breast cancer, which significantly improve survival. However, diagnosing the molecular status of breast cancer involves a pathological biopsy, which can affect the accuracy of the diagnosis. To non-invasively diagnose the hormone receptor (HR) status of breast cancer and reduced manual annotation, we proposed a weakly supervised deep learning framework BSNet which detected breast cancer with HR status and benign tumors. BSNet was trained on 2321 multi-view mammography cases from female undergoing digital mammography for the general population at Harbin Medical University Cancer Hospital in Heilongjiang Province during the period 2017-2018 and was validated on the external cohort. The average AUCs of BSNet on the test set and the external validation set were 0.89 and 0.92, respectively. BSNet demonstrated excellent performance in non-invasive breast cancer diagnosis with HR status, using multiple mammography views without pixel annotation. Furthermore, we developed a web server (http://bsnet.edbc.org) for easy use. BSNet described high-dimensional mammography of breast cancer subtypes, which helped inform early management options.© 2023 The Authors.