AI 中文总结
COGENT是基于MedGS和Sybil模型的框架，通过高斯体积表示的反事实优化，为肺部CT扫描提供具有临床意义的稀疏可解释性，优于现有方法。
AI 中文摘要
可解释性对于将深度学习模型部署到高风险医疗应用中至关重要。现有的体积成像可解释性方法主要在体素空间中运行，忽略了3D场景建模最新进展所引入的结构化表示。我们提出COGENT（Counterfactual Gaussian Explanations，反事实高斯解释），这一框架可直接在基于高斯的体积表示的参数空间中生成反事实解释。该框架基于MedGS和Sybil肺癌风险预测模型构建，通过可微渲染流水线优化选定的高斯基元，使来自下游预测器的梯度能够识别对模型决策影响最大的表示组件。与传统的像素级或体素级归因方法不同，我们的方法将可解释性表述为针对显式3D场景表示的反事实优化问题，生成稀疏且空间定位的解释，同时保留解剖学一致性。我们使用肺部CT扫描对COGENT进行评估，将其与现有可解释性方法进行定量比较，并由医学专家进行定性分析。结果表明，表示空间反事实优化可提供具有临床意义的解释，同时为解释体积深度学习模型提供了新视角。
英文摘要
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.
