ReMoDEx: A Local-to-Global Relevance-Based Model Decision Explainability Framework for large-Scale Image Datasets

2026-07-08 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

ReMoDEx is a new framework designed for systematic, dataset-scale assessment of deep learning image classifier decisions, addressing the opacity of models on large datasets where manual heatmap inspection is impractical. It combines local explainability methods like GradCAM++, Integrated Gradients, Occlusion Sensitivity, and Layerwise Relevance Propagation with a global module that groups relevance maps into decision strategy clusters. This pipeline includes model inference, target class selection, relevance map generation, heatmap standardization, similarity-based grouping, cluster-level interpretation, and spatial relevance assessment. Applied to a VGG16 classifier distinguishing COVID-19, Normal, Lung Opacity, and Viral Pneumonia, ReMoDEx revealed two recurring decision strategies—central thoracic region and border/corner sensitive decisions—despite the model achieving 86.27% test accuracy and 0.9624 test AUC, indicating potential shortcut learning.

Key takeaway

For AI Scientists and Computer Vision Engineers evaluating deep learning image classifiers for critical applications, you should integrate frameworks like ReMoDEx into your validation pipeline. Relying solely on accuracy metrics can mask problematic decision strategies, such as shortcut learning from peripheral image regions. Use ReMoDEx to systematically uncover and address these hidden biases, ensuring your models make robust, task-relevant decisions before deployment.

Key insights

ReMoDEx offers a scalable framework to assess deep learning model decision behavior beyond accuracy, identifying shortcut learning.

Principles

• Model accuracy alone is insufficient for trustworthiness.
• Local explainers can be scaled globally for dataset insights.
• Shortcut learning can manifest as peripheral region reliance.

Method

ReMoDEx's pipeline involves inference, target class selection, relevance map generation, heatmap standardization, similarity grouping, cluster interpretation, and spatial relevance assessment.

In practice

• Apply ReMoDEx to large-scale medical imaging datasets.
• Validate model decisions by occluding identified regions.
• Identify shortcut learning in high-performing classifiers.

Topics

• ReMoDEx
• Model Explainability
• Image Classification
• Shortcut Learning
• Medical Imaging
• Deep Learning Explainers

Best for: AI Scientist, Computer Vision Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.