Add to ORKGThe need for clear, trustworthy explanations of deep learning model predictions is essential for high-criticality fields, such as medicine and biometric identification. Class Activation Maps (CAMs) are an increasingly popular category of visual explanation methods for Convolutional Neural Networks (CNNs). However, the performance of individual CAMs depends largely on experimental parameters such as the selected image, target class, and model. Here, we propose MetaCAM, an ensemble-based method for combining multiple existing CAM methods based on the consensus of the top-k% most highly activated pixels across component CAMs. We perform experiments to quantifiably determine the optimal combination of 11 CAMs for a given MetaCAM experiment. A n...
Purpose Existing class activation mapping (CAM) techniques extract the feature maps only from a sing...
CNNs and other deep learners are now state-of-the-art in medical imaging research. However, the smal...
Deep neural networks are ubiquitous due to the ease of developing models and their influence on othe...
The need for clear, trustworthy explanations of deep learning model predictions is essential for hig...
Recent research in deep learning methodology has led to a variety of complex modelling techniques in...
The need for Explainable AI is increasing with the development of deep learning. The saliency maps d...
Class Activation Mapping (CAM) can be used to obtain a visual understanding of the predictions made ...
Deep learning (DL) models achieve remarkable performance in classification tasks. However, models wi...
Graph convolutional neural network (GCN) has drawn increasing attention and attained good performanc...
In this paper two new learning-based eXplainable AI (XAI) methods for deep convolutional neural netw...
Classification networks have been used in weakly-supervised semantic segmentation (WSSS) to segment ...
In recent years, artificial intelligence is increasingly being applied widely in many different fiel...
Radiology report generation (RRG) has gained increasing research attention because of its huge poten...
Extracting class activation maps (CAM) is a key step for weakly-supervised semantic segmentation (WS...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
Purpose Existing class activation mapping (CAM) techniques extract the feature maps only from a sing...
CNNs and other deep learners are now state-of-the-art in medical imaging research. However, the smal...
Deep neural networks are ubiquitous due to the ease of developing models and their influence on othe...
The need for clear, trustworthy explanations of deep learning model predictions is essential for hig...
Recent research in deep learning methodology has led to a variety of complex modelling techniques in...
The need for Explainable AI is increasing with the development of deep learning. The saliency maps d...
Class Activation Mapping (CAM) can be used to obtain a visual understanding of the predictions made ...
Deep learning (DL) models achieve remarkable performance in classification tasks. However, models wi...
Graph convolutional neural network (GCN) has drawn increasing attention and attained good performanc...
In this paper two new learning-based eXplainable AI (XAI) methods for deep convolutional neural netw...
Classification networks have been used in weakly-supervised semantic segmentation (WSSS) to segment ...
In recent years, artificial intelligence is increasingly being applied widely in many different fiel...
Radiology report generation (RRG) has gained increasing research attention because of its huge poten...
Extracting class activation maps (CAM) is a key step for weakly-supervised semantic segmentation (WS...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
Purpose Existing class activation mapping (CAM) techniques extract the feature maps only from a sing...
CNNs and other deep learners are now state-of-the-art in medical imaging research. However, the smal...
Deep neural networks are ubiquitous due to the ease of developing models and their influence on othe...