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. Author manuscript; available in PMC: 2020 Mar 26.
Published in final edited form as: J Am Coll Cardiol. 2019 Mar 26;73(11):1317–1335. doi: 10.1016/j.jacc.2018.12.054

FIGURE 7. Training of a Deep Convolutional Neural Network.

FIGURE 7

Patterns of SPECT perfusion defects are identified by feature extraction (left) into a deep learning process (center) that combines parameters of location, shape, and density. This generates a probability of obstructive coronary artery disease in the left anterior descending artery (LAD), left circumflex artery (LCx), and right coronary artery (RCA) territories (right), which is trained by obstructive stenosis correlations by invasive coronary angiography. FC = fully connected layer; Max-pooling = filter that retains only the maximum value in a 2 × 2 patch; QPS = quantitative perfusion SPECT; ReLU = rectified linear unit (linear function mapping input to output values with a threshold). Adapted with permission from Betancur et al. (82).