Improving ultrasound video classification: an evaluation of novel deep learning methods in echocardiography.

ABSTRACT: Echocardiography is the commonest medical ultrasound examination, but automated interpretation is challenging and hinges on correct recognition of the 'view' (imaging plane and orientation). Current state-of-the-art methods for identifying the view computationally involve 2-dimensional convolutional neural networks (CNNs), but these merely classify individual frames of a video in isolation, and ignore information describing the movement of structures throughout the cardiac cycle. Here we explore the efficacy of novel CNN architectures, including time-distributed networks and two-stream networks, which are inspired by advances in human action recognition. We demonstrate that these new architectures more than halve the error rate of traditional CNNs from 8.1% to 3.9%. These advances in accur

SUBMITTER: Howard JP 

PROVIDER: S-EPMC7100611 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature