BACKGROUND AND OBJECTIVE
Deep learning based medical image analysis technologies have the potential to greatly improve the workflow of neuro-radiologists dealing routinely with multi-sequence MRI. However, an essential step for current deep learning systems employing multi-sequence MRI is to ensure that their sequence type is correctly assigned. This requirement is not easily satisfied in clinical practice and is subjected to protocol and human-prone errors. Although deep learning models are promising for image-based sequence classification, robustness, and reliability issues limit their application to clinical practice.
METHODS
In this paper, we propose a novel method that uses saliency information to guide the learning of features for sequence classification. The method uses two self-supervised loss terms to first enhance the distinctiveness among class-specific saliency maps and, secondly, to promote similarity between class-specific saliency maps and learned deep features.
RESULTS
On a cohort of 2100 patient cases comprising six different MR sequences per case, our method shows an improvement in mean accuracy by 4.4% (from 0.935 to 0.976), mean AUC by 1.2% (from 0.9851 to 0.9968), and mean F1 score by 20.5% (from 0.767 to 0.924). Furthermore, based on feedback from an expert neuroradiologist, we show that the proposed approach improves the interpretability of trained models as well as their calibration with reduced expected calibration error (by 30.8%, from 0.065 to 0.045). The code will be made publicly available.
CONCLUSIONS
In this paper, the proposed method shows an improvement in accuracy, AUC, and F1 score, as well as improved calibration and interpretability of resulting saliency maps
