用贝叶斯深度学习捕捉不确定信息，提升乳腺肿块BI-RADS评分预测准确率。
BI-RADS prediction of mammographic masses using uncertainty information extracted from a Bayesian Deep Learning model
- 通过贝叶斯模型提取不确定性信息辅助诊断
- 对BI-RADS 2/3/5类别的预测F1最高达73.33%
- 可区分BI-RADS 0类中良恶性，且全检出恶性病例
BI_RADS评分是放射科医生基于乳腺钼靶图像形态特征判断乳腺癌风险的量化工具，但对病灶描述存在显著差异，常导致评分误判。本研究利用贝叶斯深度学习模型提取的不确定性信息进行BI_RADS评分预测。基于病理结果的评估显示，放射科医生在BI_RADS 2、3、5类别上的F1分数分别为42.86%、48.33%和48.28%，而模型对应分数为73.33%、59.60%和59.26%。此外，模型在所用数据集中对BI_RADS 0类样本的良恶性区分准确率达75.86%，并能将所有恶性样本正确识别为BI_RADS 5。Grad-CAM可视化表明模型关注病灶的形态特征。结果表明，该不确定性感知的贝叶斯深度学习模型能像放射科医生一样，根据形态特征对病变恶性程度给出不确定性判断。
原文摘要 · Abstract (English)
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.
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