GANterfactual—Counterfactual Explanations for Medical Non-experts Using Generative Adversarial Learning
Open accessAbstract
With the ongoing rise of machine learning, the need for methods for explaining decisions made by artificial intelligence systems is becoming a more and more important topic. Especially for image classification tasks, many state-of-the-art tools to explain such classifiers rely on visual highlighting of important areas of the input data. Contrary, counterfactual explanation systems try to enable a counterfactual reasoning by modifying the input image in a way such that the classifier would have made a different prediction. By doing so, the users of counterfactual explanation systems are equipped with a completely different kind of explanatory information. However, methods for generating realistic counterfactual explanations for image classifiers are still rare. Especially in medical contexts, where relevant information often consists of textural and structural information, high-quality counterfactual images have the potential to give meaningful insights into decision processes. In this work, we present GANterfactual , an approach to generate such counterfactual image explanations based on adversarial image-to-image translation techniques. Additionally, we conduct a user study to evaluate our approach in an exemplary medical use case. Our results show that, in the chosen medical use-case, counterfactual explanations lead to significantly better results regarding mental models, explanation satisfaction, trust, emotions, and self-efficacy than two state-of-the art systems that work with saliency maps, namely LIME and LRP.
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Counterfactual explanation methods such as GANterfactual lack flexibility and are strongly model-specific because a slight change in task would require a new dataset and retraining of the CycleGAN and binary classifier.
“However, a limitation of this method is its lack of flexibility and strong model-specificity since a slight change in the task would require the acquisition of a new dataset alongside another training of the CycleGAN and the binary classifier.”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [32]
In a user study, counterfactual explanations led to significantly better results regarding mental models, explanation satisfaction, and trust compared with Layerwise Relevance Propagation and Local Interpretable Model-agnostic Explanations.
“The authors stated that counterfactual explanations led to significantly better results regarding mental models, explanation satisfaction, and trust.”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [32]
GANterfactuals depend on a binary classifier.
“Dependence on binary classifier”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [32]
GANterfactuals provide intuitive explainability through a contrastive setting, even for non-experts.
“Intuitive explainability by contrastive setting, even for non-experts”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [32]
GANterfactuals lack flexibility.
“Lack of flexibility”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [32]
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- Authors
- Mertes, S., Huber, T., Weitz, K., Heimerl, A., & André, E.
- Affiliations
- University of Augsburg (DE)
- Corresponding author
- Mertes, Silvan
- Venue
- Frontiers in Artificial Intelligence
- Publisher
- Frontiers Media SA
- Published
- 2022-04-08
- Volume
- 5
- Pages
- 825565-825565
- ISSN
- 2624-8212 (ISSN-L)
- PMC
- PMC9024220
- OpenAlex
- W3187709298
- Cited by
- 119
- References
- 53
- Citations by year
- 2026: 32 · 2025: 34 · 2024: 31 · 2023: 16 · 2022: 6
- FWCI
- 7.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Artificial Intelligence (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- OPUS (Augsburg University) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Deutsche Forschungsgemeinschaft
- Bayerisches Staatsministerium für Wissenschaft, Forschung und Kunst
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- counterfactual explanations, generative adversarial networks, image-to-image translation, medical image classification, explainable AI, adversarial perturbation generation, non-expert users, user study, mental models, trust in AI, self-efficacy, saliency maps, lime, LRP
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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