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Found 2 papers, 1 papers with code
Discovering and interpreting transcriptomic drivers of imaging traits using neural networks
To interpret the models, we propose gene masking and gene saliency to extract learned relationships from radiogenomic neural networks.
Quantitative Methods
Intra-Domain Task-Adaptive Transfer Learning to Determine Acute Ischemic Stroke Onset Time
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.
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