Grad-CAM
A gradient-based visual interpretability method that computes gradients of a target class with respect to the final convolutional feature maps to generate saliency heatmaps, localizing image regions that contribute most to the model's prediction.
Timeline (last 90 days)
Pixel-level attribution methods such as LIME, SHAP, and Grad-CAM use pixels or regions as the unit of explanation rather than semantic concepts familiar to humans.
Commonly used interpretability mitigation measures for medical AI in the industry include attention visualization (Grad-CAM), SHAP value explanations, and "human-in-the-loop" design.
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Pixel-level attribution methods such as LIME, SHAP, and Grad-CAM use pixels or regions as the unit of explanation rather than semantic concepts familiar to humans.
50%UnverifiedCommonly used interpretability mitigation measures for medical AI in the industry include attention visualization (Grad-CAM), SHAP value explanations, and "human-in-the-loop" design.
50%UnverifiedGrad-CAM由佐治亚理工学院研究团队于2017年提出，利用目标类别对最后一层卷积特征图的梯度信息生成热力图，显示模型关注的图像区域
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