TTE-CAM: Self-Explainable Class Activation Maps for Pretrained Black-Box CNNs

Kerol Djoumessi; Philipp Berens

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arXiv · 2603.26885

TTE-CAM: Self-Explainable Class Activation Maps for Pretrained Black-Box CNNs

Kerol Djoumessi·Philipp Berens

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Abstract

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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Kerol Djoumessi, Philipp Berens. 2026-03-27. TTE-CAM: Self-Explainable Class Activation Maps for Pretrained Black-Box CNNs. https://arxiv.org/abs/2603.26885

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