We apply this approach to both 2D and 3D CNN architectures with our top model achieving an ROC-AUC value of 0. 74, with a sensitivity of 0. 70 and a specificity of 0. 81 for classifying TSS < 4. 5 hours.
To interpret the models, we propose gene masking and gene saliency to extract learned relationships from radiogenomic neural networks.