让预训练的黑盒CNN模型在推理时自动生成可信解释。
TTE-CAM: Self-Explainable Class Activation Maps for Pretrained Black-Box CNNs
- 用卷积模块替换分类头，实现测试时自解释
- 保持原有预测性能的同时生成高质量激活图
- 适合医疗图像分析等高风险场景的可解释需求
卷积神经网络在医学图像分析中表现优异，但其决策过程不透明，限制了在高风险临床场景中的应用。现有方法存在根本矛盾：后处理解释方法虽易实现但不够准确，而内建可解释架构虽忠实却常牺牲性能。我们提出TTE-CAM，一种测试时框架，通过用基于卷积的模块替换预训练黑盒CNN的分类头（初始权重来自原模型），将黑盒模型转化为自解释模型。该方法在保持原有预测性能的同时，生成与后处理方法相当且更忠实的类激活图，在定性和定量上均表现优秀。代码已开源。
原文摘要 · Abstract (English)
Convolutional neural networks (CNNs) achieve state-of-the-art performance in medical image analysis yet remain opaque, limiting adoption in high-stakes clinical settings. Existing approaches face a fundamental trade-off: post-hoc methods provide unfaithful approximate explanations, while inherently interpretable architectures are faithful but often sacrifice predictive performance. We introduce TTE-CAM, a test-time framework that bridges this gap by converting pretrained black-box CNNs into self-explainable models via a convolution-based replacement of their classification head, initialized from the original weights. The resulting model preserves black-box predictive performance while delivering built-in faithful explanations competitive with post-hoc methods, both qualitatively and quantitatively. The code is available at https://github.com/kdjoumessi/Test-Time-Explainability
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