The BI_RADS score is a probabilistic reporting tool used by radiologists to express the level of uncertainty in predicting breast cancer based on some morphological features in mammography images. There is a significant variability in describing masses which sometimes leads to BI_RADS misclassification. Using a BI_RADS prediction system is required to support the final radiologist decisions. In this study, the uncertainty information extracted by a Bayesian deep learning model is utilized to predict the BI_RADS score. The investigation results based on the pathology information demonstrate that the f1-scores of the predictions of the radiologist are 42.86%, 48.33% and 48.28%, meanwhile, the f1-scores of the model performance are 73.33%, 59.60% and 59.26% in the BI_RADS 2, 3 and 5 dataset samples, respectively. Also, the model can distinguish malignant from benign samples in the BI_RADS 0 category of the used dataset with an accuracy of 75.86% and correctly identify all malignant samples as BI_RADS 5. The Grad-CAM visualization shows the model pays attention to the morphological features of the lesions. Therefore, this study shows the uncertainty-aware Bayesian Deep Learning model can report his uncertainty about the malignancy of a lesion based on morphological features, like a radiologist.

翻译：BI-RADS评分是放射科医师基于乳腺X线影像中某些形态学特征预测乳腺癌时，用于表达不确定性水平的概率性报告工具。对肿块的描述存在显著差异性，有时会导致BI-RADS分类错误。需要采用BI-RADS预测系统来辅助放射科医师的最终决策。本研究利用贝叶斯深度学习模型提取的不确定性信息来预测BI-RADS评分。基于病理信息的调查结果表明：在BI-RADS 2、3、5级数据集样本中，放射科医师预测的f1分数分别为42.86%、48.33%和48.28%，而模型性能的f1分数分别为73.33%、59.60%和59.26%。此外，该模型在所使用数据集的BI-RADS 0类别中区分恶性与良性样本的准确率达到75.86%，并能将所有恶性样本正确识别为BI-RADS 5级。Grad-CAM可视化显示模型关注病灶的形态学特征。因此，本研究表明这种不确定性感知的贝叶斯深度学习模型能够像放射科医师一样，基于形态学特征报告其对病灶恶性程度的不确定性。