BI-RADS prediction of mammographic masses using uncertainty information extracted from a Bayesian Deep Learning model
This work addresses the need for automated support in breast cancer diagnosis to reduce radiologist variability, though it is incremental as it applies an existing Bayesian method to a specific medical imaging task.
The study tackled the problem of variability and misclassification in BI-RADS scoring for mammographic masses by using a Bayesian deep learning model to predict BI-RADS scores, achieving f1-scores of up to 73.33% for BI-RADS 2 and an accuracy of 75.86% for distinguishing malignant from benign samples in BI-RADS 0.
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.