Efficient Echocardiogram View Classification with Sampling-Free Uncertainty Estimation

Ang Nan Gu, Christina Luong, Mohammad H. Jafari, Nathan Van Woudenberg, Hany Girgis, Purang Abolmaesumi, Teresa Tsang

Abstract: View classification is a key initial step for the analysis of echocardiograms. Typical deep learning classifiers can make highly confident errors unnoticed by human operators, consequential for downstream tasks. Instead of failing, it is important to create a method that alarms “I don’t know” to inform clinicians of potential errors when faced with difficult or novel inputs. This paper proposes Efficient-Evidential Network (Efficient-EvidNet), a lightweight framework designed to classify echocardiogram views and simultaneously provide a sampling-free uncertainty prediction. Evidential uncertainty is used to filter faulty results and flag out the outliers, hence, improving the overall performance. Efficient-EvidNet classifies among 13 standard echo views with 91.9% test accuracy, competitive with other state-of-the-art lightweight networks. Notably, it achieves a 97.6% test accuracy when only reporting on data with low evidential uncertainty. Further, we propose improved techniques for outlier detection, reaching a 0.97 area under the ROC curve for differentiating between cardiac and lung ultrasound, for which the latter is unseen throughout the training. Efficient-EvidNet does not require costly sampling steps for uncertainty estimation and uses a low parameter neural network, providing two key features that are essential for real-time deployment in clinical scenarios.