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Found 7 papers, 4 papers with code
Augmentation by Counterfactual Explanation -- Fixing an Overconfident Classifier
The classification outcome should reflect a high uncertainty on ambiguous in-distribution samples that lie close to the decision boundary.
Deep Learning for Medical Imaging From Diagnosis Prediction to its Counterfactual Explanation
Deep neural networks (DNN) have achieved unprecedented performance in computer-vision tasks almost ubiquitously in business, technology, and science.
Using Causal Analysis for Conceptual Deep Learning Explanation
Model explainability is essential for the creation of trustworthy Machine Learning models in healthcare.
Self-Supervised Vessel Enhancement Using Flow-Based Consistencies
Our experiments on various public datasets in 2D and 3D show that our method performs better than unsupervised methods while learning useful transferable features from unlabeled data.
Explaining the Black-box Smoothly- A Counterfactual Approach
We adopted a Generative Adversarial Network (GAN) to generate a progressive set of perturbations to a query image, such that the classification decision changes from its original class to its negation.
Explanation by Progressive Exaggeration
As machine learning methods see greater adoption and implementation in high stakes applications such as medical image diagnosis, the need for model interpretability and explanation has become more critical.
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector
Our model consists of three mutually dependent modules which regulate each other: (1) a discriminative network that learns a fixed-length representation from local features and maps them to disease severity; (2) an attention mechanism that provides interpretability by focusing on the areas of the anatomy that contribute the most to the prediction task; and (3) a generative network that encourages the diversity of the local latent features.
