Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models

Core Concepts

The MiMICRI framework provides domain-centered counterfactual explanations of cardiovascular image classification models to help users understand and validate model predictions based on relevant morphological features.

Abstract

The paper proposes the MiMICRI framework for generating domain-centered counterfactual explanations of cardiovascular image classification models. The key components of the framework are: Image Segmentation: Identify domain-relevant morphological features in cardiac MRI images, such as the left ventricle (LV) cavity, LV myocardium, and right ventricle (RV) cavity. Feature Selection: Allow users to interactively select the image segments they want to replace in a target image. Image Recombination: Replace the selected segments in the target image with corresponding segments from source images to generate recombined images. Counterfactual Inspection: Use the original classification model to predict labels for the recombined images. Recombined images with a different predicted label from the original target image are considered counterfactuals. The authors implemented the MiMICRI framework as a Python visualization package and evaluated it with two medical experts. The experts found that the domain-centered counterfactual explanations helped them reason about model predictions in terms of relevant morphological features and medical knowledge. However, they also raised concerns about the clinical plausibility of the recombined images due to the structural interdependence of cardiac segments. The paper discusses the implications of these findings on the generalizability, trustworthiness, and development of domain-centered XAI methods for enhancing model interpretability in healthcare contexts.

Stats

"The model achieved a performance of accuracy 0.87 and auroc 0.65." "In total, we generated 23226 recombined images (21 hypertension × 79 no hypertension × 2 runs × 7 segment combinations)."

Quotes

"you'd have to do that within people with similar other clinical characteristics so that you're not biasing your data set and then having [the model] predict off the clinical factors" "the LV myocardium and blood pool (cavity) are completely interrelated because one bounds the other, so you can't change one without the other." "though that may not affect segmentation, it would likely affect any whole-image analysis"

Key Insights Distilled From

MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models

by Grace Guo,Li... at arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16174.pdf

Deeper Inquiries

How can the MiMICRI framework be extended to other medical imaging modalities beyond cardiac MRI, while ensuring the generated counterfactuals remain clinically plausible?

The extension of the MiMICRI framework to other medical imaging modalities involves several key considerations to ensure the generated counterfactuals remain clinically plausible. Segmentation Algorithm Optimization: Different medical imaging modalities may require specific segmentation algorithms tailored to the unique features of the images. By optimizing the segmentation algorithms for each modality, the framework can accurately identify and replace relevant image segments. Domain Expert Collaboration: Collaboration with domain experts in each medical imaging modality is crucial. These experts can provide insights into the anatomical structures and physiological relationships within the images, guiding the selection of segments for replacement to ensure clinical plausibility. Structural Interdependence Awareness: Understanding the structural interdependence of image segments is essential. For modalities with complex anatomical relationships, the framework should account for how changes in one segment may affect others, ensuring that the recombined images reflect realistic physiological scenarios. Validation Studies: Conducting validation studies with medical professionals in each imaging modality can help assess the clinical plausibility of the generated counterfactuals. Feedback from experts can identify areas where the framework may need adjustments to improve accuracy and relevance. Data Augmentation Techniques: Implementing data augmentation techniques specific to each modality can enhance the diversity and realism of the recombined images. Techniques such as rotation, scaling, and flipping can be applied to create variations that mimic real-world scenarios. By incorporating these strategies, the MiMICRI framework can be effectively extended to various medical imaging modalities while maintaining the clinical plausibility of the generated counterfactual explanations.

How can domain-centered XAI methods like MiMICRI be designed to automatically detect and analyze relevant subgroups within the data, without relying on users to manually specify the subgroup characteristics?

Automating the detection and analysis of relevant subgroups within the data in domain-centered XAI methods like MiMICRI can enhance efficiency and accuracy. Here are some approaches to achieve this automation: Machine Learning Models: Implement machine learning models, such as clustering algorithms or decision trees, to automatically detect patterns and subgroups within the data. These models can analyze features and identify distinct subgroups based on similarities or differences in the data. Feature Engineering: Develop automated feature engineering techniques that extract relevant characteristics from the data to identify subgroups. By analyzing the data's intrinsic properties, the framework can automatically detect and categorize subgroups without manual intervention. Unsupervised Learning: Utilize unsupervised learning algorithms like k-means clustering or hierarchical clustering to partition the data into meaningful subgroups based on similarities. These algorithms can automatically group data points without the need for predefined subgroup characteristics. Pattern Recognition: Implement pattern recognition algorithms that can identify common patterns or anomalies within the data, leading to the automatic detection of subgroups. By recognizing recurring patterns, the framework can categorize data into relevant subgroups. Continuous Learning: Incorporate continuous learning mechanisms that adapt to new data and evolving patterns over time. By continuously analyzing and updating subgroup information, the framework can dynamically adjust to changes in the data distribution. By integrating these automated techniques into domain-centered XAI methods like MiMICRI, the framework can autonomously detect and analyze relevant subgroups within the data, enhancing its ability to provide tailored and insightful explanations without manual subgroup specification.

What additional techniques or user interactions could be incorporated into MiMICRI to better assess the trustworthiness of both the model and the generated counterfactual explanations?

To enhance the assessment of trustworthiness in MiMICRI, additional techniques and user interactions can be incorporated: Confidence Intervals: Provide confidence intervals for model predictions and counterfactual explanations. Users can assess the uncertainty associated with the model's predictions and the reliability of the generated counterfactuals. Sensitivity Analysis: Conduct sensitivity analysis to evaluate how variations in input data impact model predictions. By analyzing the model's sensitivity to different inputs, users can gain insights into its robustness and reliability. Explanation Consistency: Ensure consistency in the explanations provided by MiMICRI. Users should be able to compare multiple counterfactuals for the same input to assess the stability and consistency of the model's behavior. Explanation Visualization: Incorporate visualizations that highlight the key features influencing model predictions. Visual representations of the counterfactual explanations can help users interpret and validate the model's decisions more effectively. User Feedback Mechanism: Implement a feedback mechanism where users can provide input on the trustworthiness of the explanations. User feedback can help refine the model and the explanation generation process based on real-world insights. Model Performance Metrics: Display performance metrics such as accuracy, precision, recall, and F1 score to evaluate the model's overall performance. Users can use these metrics to gauge the model's trustworthiness and effectiveness. Explanation Validation: Allow users to validate the generated counterfactual explanations against external sources or domain knowledge. By enabling users to cross-reference the explanations with established facts, the trustworthiness of the explanations can be verified. By integrating these techniques and user interactions into MiMICRI, users can better assess the trustworthiness of both the model and the generated counterfactual explanations, enhancing the transparency and reliability of the XAI framework.