TTE-CAM: Self-Explainable Class Activation Maps for Pretrained Black-Box CNNs
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866916063322570752 |
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| author | Djoumessi, Kerol Berens, Philipp |
| author_facet | Djoumessi, Kerol Berens, Philipp |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26885 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TTE-CAM: Self-Explainable Class Activation Maps for Pretrained Black-Box CNNs Djoumessi, Kerol Berens, Philipp Computer Vision and Pattern Recognition 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 |
| title | TTE-CAM: Self-Explainable Class Activation Maps for Pretrained Black-Box CNNs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.26885 |