# Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation

arXiv: https://arxiv.org/abs/2306.07306 · HTML: https://mlsanity.com/paper/2306.07306/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 48 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Interpretable Explanations of Black Boxes by Meaningful Perturbation](https://mlsanity.com/paper/1704.03296/) (2017)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [The (Un)reliability of saliency methods](https://mlsanity.com/paper/1711.00867/) (2017)
- [Distilling a Neural Network Into a Soft Decision Tree](https://mlsanity.com/paper/1711.09784/) (2017)
- [Swapping Autoencoder for Deep Image Manipulation](https://mlsanity.com/paper/2007.00653/) (2020)
- [Full-Gradient Representation for Neural Network Visualization](https://mlsanity.com/paper/1905.00780/) (2019)
- [Visualizing Deep Neural Network Decisions: Prediction Difference Analysis](https://mlsanity.com/paper/1702.04595/) (2017)
- [Explaining Image Classifiers by Counterfactual Generation](https://mlsanity.com/paper/1807.08024/) (2018)
- [Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model](https://mlsanity.com/paper/1511.01644/) (2015)
- [Explainable deep learning models in medical image analysis](https://mlsanity.com/paper/2005.13799/) (2020)
- [Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation](https://mlsanity.com/paper/1710.06169/) (2017)
- [Improving the Interpretability of Deep Neural Networks with Knowledge Distillation](https://mlsanity.com/paper/1812.10924/) (2018)
- [Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks](https://mlsanity.com/paper/1712.06302/) (2017)
- [Distilling Knowledge from Deep Networks with Applications to Healthcare Domain](https://mlsanity.com/paper/1512.03542/) (2015)
- [Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack](https://mlsanity.com/paper/2103.03438/) (2021)

## Abstract (arXiv)

Explainability poses a major challenge to artificial intelligence (AI) techniques. Current studies on explainable AI (XAI) lack the efficiency of extracting global knowledge about the learning task, thus suffer deficiencies such as imprecise saliency, context-aware absence and vague meaning. In this paper, we propose the class association embedding (CAE) approach to address these issues. We employ an encoder-decoder architecture to embed sample features and separate them into class-related and individual-related style vectors simultaneously. Recombining the individual-style code of a given sample with the class-style code of another leads to a synthetic sample with preserved individual characters but changed class assignment, following a cyclic adversarial learning strategy. Class association embedding distills the global class-related features of all instances into a unified domain with well separation between classes. The transition rules between different classes can be then extracted and further employed to individual instances. We then propose an active XAI framework which manipulates the class-style vector of a certain sample along guided paths towards the counter-classes, resulting in a series of counter-example synthetic samples with identical individual characters. Comparing these counterfactual samples with the original ones provides a global, intuitive illustration to the nature of the classification tasks. We adopt the framework on medical image classification tasks, which show that more precise saliency maps with powerful context-aware representation can be achieved compared with existing methods. Moreover, the disease pathology can be directly visualized via traversing the paths in the class-style space.