Model Interpretability: Grad-CAM and Attention Mechanisms
Use Grad-CAM and attention visualizations to explain CNN predictions and build clinician trust in AI recommendations.
In regulated healthcare settings, 'black box' predictions are unacceptable. Clinicians must understand why an AI system flags an abnormality. Grad-CAM (Gradient-weighted Class Activation Mapping) highlights image regions most influential to a CNN's decision by computing gradients of the target class with respect to feature maps. The resulting heatmap shows where the network 'looked' when classifying. Attention mechanisms, native to transformer architectures, learn to weight different regions or features automatically, producing interpretable attention maps. For example, a chest X-ray classification network's Grad-CAM might highlight a subtle nodule the model identified as suspicious, validating clinical concerns. Conversely, if Grad-CAM shows the network…
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