Olivier X Miguel, Emily Kaczmarek, Inok Lee, Robin Ducharme, Alysha L J Dingwall-Harvey, Ruth Rennicks White, Brigitte Bonin, Richard I Aviv, Steven Hawken, Christine M Armour, Kevin Dick, Mark C Walker
Deep learning algorithms have demonstrated remarkable potential in clinical diagnostics, particularly in the field of medical imaging. In this study, we investigated the application of deep learning models in early detection of fetal kidney anomalies. To provide an enhanced interpretation of those models' predictions, we proposed an adapted two-class representation and developed a multi-class model interpretation approach for problems with more than two labels and variable hierarchical grouping of labels. Additionally, we employed the explainable AI (XAI) visualization tools Grad-CAM and HiResCAM, to gain insights into model predictions and identify reasons for misclassifications...
April 19, 2024: Scientific Reports