ProtoASNet: Dynamic Prototypes for Inherently Interpretable and
  Uncertainty-Aware Aortic Stenosis Classification in Echocardiography
Hooman Vaseli Ang Nan Gu S. Neda Ahmadi Amiri Michael Y. Tsang Andrea Fung Nima Kondori Armin Saadat Purang Abolmaesumi Teresa S. M. Tsang
Aortic stenosis (AS) is a common heart valve disease that requires accurate
and timely diagnosis for appropriate treatment. Most current automatic AS
severity detection methods rely on black-box models with a low level of
trustworthiness, which hinders clinical adoption. To address this issue, we
propose ProtoASNet, a prototypical network that directly detects AS from B-mode
echocardiography videos, while making interpretable predictions based on the
similarity between the input and learned spatio-temporal prototypes. This
approach provides supporting evidence that is clinically relevant, as the
prototypes typically highlight markers such as calcification and restricted
movement of aortic valve leaflets. Moreover, ProtoASNet utilizes abstention
loss to estimate aleatoric uncertainty by defining a set of prototypes that
capture ambiguity and insufficient information in the observed data. This
provides a reliable system that can detect and explain when it may fail. We
evaluate ProtoASNet on a private dataset and the publicly available TMED-2
dataset, where it outperforms existing state-of-the-art methods with an
accuracy of 80.0% and 79.7%, respectively. Furthermore, ProtoASNet provides
interpretability and an uncertainty measure for each prediction, which can
improve transparency and facilitate the interactive usage of deep networks to
aid clinical decision-making. Our source code is available at:
https://github.com/hooman007/ProtoASNet.