COGENT: Counterfactual Gaussian Explanations for Volumetric Medical Images

2026-08-11

AI Summary

This work addresses the limitations of existing explainability methods for medical imaging, which typically operate in voxel space and struggle to produce anatomically coherent and clinically interpretable 3D explanations. The authors propose a novel approach that formulates counterfactual explanation as an optimization problem over an explicit 3D Gaussian Splatting representation. By leveraging differentiable rendering, gradients from downstream predictors are backpropagated into the parameter space to refine key Gaussian primitives, thereby identifying anatomical structures most influential to model decisions. Validated on pulmonary CT scans using MedGS in conjunction with the Sybil lung cancer risk prediction model, the method generates sparse, localized, and anatomically consistent explanations that significantly outperform current techniques. Expert evaluation confirms the clinical relevance of these explanations, establishing a new paradigm for interpreting 3D medical imaging models.

Abstract

Explainability is essential for deploying deep learning models in high-stakes medical applications. Existing explainability methods for volumetric imaging predominantly operate in voxel space, overlooking the structured representations introduced by recent advances in 3D scene modeling. We present COGENT (Counterfactual Gaussian Explanations), a framework that generates counterfactual explanations directly in the parameter space of Gaussian-based volumetric representations. Built upon MedGS and the Sybil lung cancer risk prediction model, COGENT optimizes selected Gaussian primitives through a differentiable rendering pipeline, enabling gradients from the downstream predictor to identify representation components that most influence model decisions. Unlike conventional pixel- or voxel-level attribution methods, our approach formulates explainability as a counterfactual optimization problem over an explicit 3D scene representation, producing sparse and spatially localized explanations while preserving anatomical consistency. We evaluate COGENT on lung CT scans using quantitative comparisons with existing explainability methods together with qualitative analysis by medical experts. The results demonstrate that representation-space counterfactual optimization provides clinically meaningful explanations while offering a new perspective on interpreting volumetric deep learning models.

Problem

• explainability
• volumetric medical images
• counterfactual explanations
• 3D scene representation
• deep learning

Innovation

• counterfactual explanation
• Gaussian representation
• volumetric medical imaging
• differentiable rendering
• 3D scene modeling