Are All Deep Learning Architectures Alike for Point-of-Care Ultrasound?: Evidence From a Cardiac Image Classification Model Suggests Otherwise.

Autor: Blaivas M; University of South Carolina School of Medicine, Columbia, South Carolina, USA.; Department of Emergency Medicine, St Francis Hospital, Columbus, Georgia, USA., Blaivas L; Michigan State University, East Lansing, Michigan, USA.
Jazyk: angličtina
Zdroj: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine [J Ultrasound Med] 2020 Jun; Vol. 39 (6), pp. 1187-1194. Date of Electronic Publication: 2019 Dec 24.
DOI: 10.1002/jum.15206
Abstrakt: Objectives: Little is known about optimal deep learning (DL) approaches for point-of-care ultrasound (POCUS) applications. We compared 6 popular DL architectures for POCUS cardiac image classification to determine whether an optimal DL architecture exists for future DL algorithm development in POCUS.
Methods: We trained 6 convolutional neural networks (CNNs) with a range of complexities and ages (AlexNet, VGG-16, VGG-19, ResNet50, DenseNet201, and Inception-v4). Each CNN was trained by using images of 5 typical POCUS cardiac views. Images were extracted from 225 publicly available deidentified POCUS cardiac videos. A total of 750,018 individual images were extracted, with 90% used for model training and 10% for cross-validation. The training time and accuracy achieved were tracked. A real-world test of the algorithms was performed on a set of 125 completely new cardiac images. Descriptive statistics, Pearson R values, and κ values were calculated for each CNN.
Results: Accuracy ranged from 96% to 85.6% correct for the 6 CNNs. VGG-16, one of the oldest and simplest CNNs, performed best at 96% correct with 232 minutes to train (R = 0.97; κ = 0.95; P < .00001). The worst-performing CNN was the newer DenseNet201, with 85.6% accuracy and 429 minutes to train (R = 0.92; κ = 0.82; P < .00001).
Conclusions: Six common image classification DL algorithms showed considerable variability in their accuracy and training time when trained and tested on identical data, suggesting that not all will perform optimally for POCUS DL applications. Contrary to well-established accuracies for CNNs, more modern and deeper algorithms yielded poorer results.
(© 2019 by the American Institute of Ultrasound in Medicine.)
Databáze: MEDLINE
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