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Found 6 papers, 4 papers with code
Be Careful When Evaluating Explanations Regarding Ground Truth
Evaluating explanations of image classifiers regarding ground truth, e. g. segmentation masks defined by human perception, primarily evaluates the quality of the models under consideration rather than the explanation methods themselves.
Explaining and visualizing black-box models through counterfactual paths
Explainable AI (XAI) is an increasingly important area of machine learning research, which aims to make black-box models transparent and interpretable.
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications
Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs).
BIG-bench Machine Learning
Explainable artificial intelligence
+2
Graph-guided random forest for gene set selection
To demonstrate a concrete application example, we focus on bioinformatics, systems biology and particularly biomedicine, but the presented methodology is applicable in many other domains as well.
The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond
Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data.
KANDINSKYPatterns -- An experimental exploration environment for Pattern Analysis and Machine Intelligence
Machine intelligence is very successful at standard recognition tasks when having high-quality training data.
