Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908306130337792 |
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| author | Najafi, Mohammad Hossein Morsali, Mohammad Pashanejad, Mohammadreza Roudi, Saman Soleimani Norouzi, Mohammad Shouraki, Saeed Bagheri |
| author_facet | Najafi, Mohammad Hossein Morsali, Mohammad Pashanejad, Mohammadreza Roudi, Saman Soleimani Norouzi, Mohammad Shouraki, Saeed Bagheri |
| contents | Deep neural networks for medical image classification often fail to generalize consistently in clinical practice due to violations of the i.i.d. assumption and opaque decision-making. This paper examines interpretability in deep neural networks fine-tuned for fracture detection by evaluating model performance against adversarial attack and comparing interpretability methods to fracture regions annotated by an orthopedic surgeon. Our findings prove that robust models yield explanations more aligned with clinically meaningful areas, indicating that robustness encourages anatomically relevant feature prioritization. We emphasize the value of interpretability for facilitating human-AI collaboration, in which models serve as assistants under a human-in-the-loop paradigm: clinically plausible explanations foster trust, enable error correction, and discourage reliance on AI for high-stakes decisions. This paper investigates robustness and interpretability as complementary benchmarks for bridging the gap between benchmark performance and safe, actionable clinical deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05483 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability Najafi, Mohammad Hossein Morsali, Mohammad Pashanejad, Mohammadreza Roudi, Saman Soleimani Norouzi, Mohammad Shouraki, Saeed Bagheri Computer Vision and Pattern Recognition Deep neural networks for medical image classification often fail to generalize consistently in clinical practice due to violations of the i.i.d. assumption and opaque decision-making. This paper examines interpretability in deep neural networks fine-tuned for fracture detection by evaluating model performance against adversarial attack and comparing interpretability methods to fracture regions annotated by an orthopedic surgeon. Our findings prove that robust models yield explanations more aligned with clinically meaningful areas, indicating that robustness encourages anatomically relevant feature prioritization. We emphasize the value of interpretability for facilitating human-AI collaboration, in which models serve as assistants under a human-in-the-loop paradigm: clinically plausible explanations foster trust, enable error correction, and discourage reliance on AI for high-stakes decisions. This paper investigates robustness and interpretability as complementary benchmarks for bridging the gap between benchmark performance and safe, actionable clinical deployment. |
| title | Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.05483 |