FeatureCAMs
FeatureCAMs, or Feature-based CAMs, are a class of explainable artificial intelligence (XAI) techniques used to interpret and understand the decision-making processes of machine learning models, particularly those based on convolutional neural networks (CNNs). Unlike traditional Class Activation Maps (CAMs), which highlight the most important regions in an image for a particular class, FeatureCAMs focus on the contribution of individual features or channels within the network.
FeatureCAMs work by computing the importance of each feature map in the final layer of the CNN.
One of the key advantages of FeatureCAMs is their ability to provide insights into the internal workings
FeatureCAMs have been applied in various domains, including medical imaging, where they can help radiologists interpret