A Neural Network That Learns to Interpret Myocardial Planar Thallium Scintigrams
Abstract
The planar thallium-201 myocardial perfusion scintigram is a widely used diagnostic technique for detecting and estimating the risk of coronary artery disease. Neural networks learned to interpret 100 thallium scinti(cid:173) grams as determined by individual expert ratings. Standard error back(cid:173) propagation was compared to standard LMS, and LMS combined with one layer of RBF units. Using the "leave-one-out" method, generaliza(cid:173) tion was tested on all 100 cases. Training time was determined automati(cid:173) cally from cross-validation perfonnance. Best perfonnance was attained by the RBF/LMS network with three hidden units per view and compares favorably with human experts.
Cite
Text
Rosenberg et al. "A Neural Network That Learns to Interpret Myocardial Planar Thallium Scintigrams." Neural Information Processing Systems, 1992.Markdown
[Rosenberg et al. "A Neural Network That Learns to Interpret Myocardial Planar Thallium Scintigrams." Neural Information Processing Systems, 1992.](https://mlanthology.org/neurips/1992/rosenberg1992neurips-neural/)BibTeX
@inproceedings{rosenberg1992neurips-neural,
title = {{A Neural Network That Learns to Interpret Myocardial Planar Thallium Scintigrams}},
author = {Rosenberg, Charles and Erel, Jacob and Atlan, Henri},
booktitle = {Neural Information Processing Systems},
year = {1992},
pages = {755-762},
url = {https://mlanthology.org/neurips/1992/rosenberg1992neurips-neural/}
}