MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models
Abstract
References
Index Terms
- MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models
Recommendations
Counterfactual explanations for misclassified images: How human and machine explanations differ
AbstractCounterfactual explanations have emerged as a popular solution for the eXplainable AI (XAI) problem of elucidating the predictions of black-box deep-learning systems because people easily understand them, they apply across different problem ...
Highlights- A novel user-centric methodology where users create counterfactual explanations.
- Human-generated counterfactuals differ significantly from machine-generated ones.
- A novel interpretation of this difference using the notion of “...
Causal generative explainers using counterfactual inference: a case study on the Morpho-MNIST dataset
AbstractIn this paper, we propose leveraging causal generative learning as an interpretable tool for explaining image classifiers. Specifically, we present a generative counterfactual inference approach to study the influence of visual features (pixels) ...
Counterfactual Explanations in Explainable AI: A Tutorial
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningDeep learning has shown powerful performances in many fields, however its black-box nature hinders its further applications. In response, explainable artificial intelligence emerges, aiming to explain the predictions and behaviors of deep learning ...
Comments
Information & Contributors
Information
Published In
![cover image ACM Other conferences](/cms/asset/33fd0f2a-33b4-4336-a458-f7b6898f6c09/3630106.cover.jpg)
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 98Total Downloads
- Downloads (Last 12 months)98
- Downloads (Last 6 weeks)55
Other Metrics
Citations
View Options
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatGet Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in