# Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability

arXiv: https://arxiv.org/abs/2504.05483 · HTML: https://mlsanity.com/paper/2504.05483/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 53 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Robustness May Be at Odds with Accuracy](https://mlsanity.com/paper/1805.12152/) (2018)
- [Captum: A unified and generic model interpretability library for PyTorch](https://mlsanity.com/paper/2009.07896/) (2020)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients](https://mlsanity.com/paper/1711.09404/) (2017)
- [A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking](https://mlsanity.com/paper/2302.14301/) (2023)
- [On the Connection Between Adversarial Robustness and Saliency Map Interpretability](https://mlsanity.com/paper/1905.04172/) (2019)
- [Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models](https://mlsanity.com/paper/2303.01870/) (2023)
- [A Light Recipe to Train Robust Vision Transformers](https://mlsanity.com/paper/2209.07399/) (2022)
- [Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors](https://mlsanity.com/paper/2006.06356/) (2020)
- [Explainable Deep Learning Methods in Medical Image Classification: A Survey](https://mlsanity.com/paper/2205.04766/) (2022)
- [Demographic Bias of Expert-Level Vision-Language Foundation Models in Medical Imaging](https://mlsanity.com/paper/2402.14815/) (2024)
- [MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification](https://mlsanity.com/paper/2406.05927/) (2024)
- [Is Grad-CAM Explainable in Medical Images?](https://mlsanity.com/paper/2307.10506/) (2023)
- [Characterizing Model Robustness via Natural Input Gradients](https://mlsanity.com/paper/2409.20139/) (2024)
- [A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systems](https://mlsanity.com/paper/2502.09849/) (2025)
- [Towards Trainable Saliency Maps in Medical Imaging](https://mlsanity.com/paper/2011.07482/) (2020)
- [Examining the Threat Landscape: Foundation Models and Model Stealing](https://mlsanity.com/paper/2502.18077/) (2025)
- [ULTra: Unveiling Latent Token Interpretability in Transformer-Based Understanding and Segmentation](https://mlsanity.com/paper/2411.12589/) (2024)
- [Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers](https://mlsanity.com/paper/2412.00472/) (2024)

## Abstract (arXiv)

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.