Article Figures & Data
Figures
- FIGURE 1.
On left is mock summary output for CNN-based risk algorithm for pulmonary embolism using ventilation and perfusion mismatch. On right is mock summary output for CNN-based risk algorithm for pulmonary embolism using low-dose CT and perfusion SPECT mismatch. Coronal and sagittal slices represent 2 different patients: one with mismatch consistent with high likelihood of pulmonary embolism (coronal), and another with matching defect associated with lower likelihood of pulmonary embolism (sagittal). PE = pulmonary embolism; V/Q = ventilation–perfusion.
- FIGURE 2.
Mock summary output for CNN-based risk algorithm for skeletal metastases, with probability classification for various outcomes and risk assessment for individual lesions. Hx = history; post inj = after injection.
- FIGURE 3.
Final ANN architecture optimizes inputs and network complexity for heart failure cohort (top) and dose extravasation cohort (bottom). Here, number of inputs, and number and complexity of nodes, are optimized. aUCR10 = area under the curve ratio between 1 and 10 min; Cend = counts at the end of the data collection; LVEF = left ventricular ejection fraction; ndAbgN = difference in counts at 4 min; Tc50 = time until reference counts are half of sensor counts.
- FIGURE 4.
Schematic representation of relationships between hardware, code, frameworks, architectures, libraries, datasets, and DL solution. DL landscape is defined by 2 orange zones, and DL ecosystem, by purple and yellow zones. CPU = central processing unit; GAN = generative adversarial network; GPU = graphics processing unit; R-CNN = region-based convolutional neural network; ResNet = residual neural network.
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