arXiv Machine Learning

CIPHER: Causal Intervention Pathways for Healthcare Equity and Robustness

arXiv:2607. 02596v1 Announce Type: cross Abstract: Deep learning models for medical diagnosis frequently exhibit substantial performance disparities across sensitive subgroups (e.

arXiv Computer Vision

Sep 3

Generating Medical Image Counterfactuals using Causal Explanations

The paper introduces a new framework for generating medical image counterfactuals that does not rely on auxiliary generative models. By extracting causal evidence directly from a classifier, the method deterministically produces edits within user-specified regions, requiring no additional training. Experiments on real-world medical imaging datasets show that these counterfactuals alter classifier predictions while staying closer to the original image than generative baselines, offering a clearer view of the model’s decision boundary.

By David A. Kelly, Tom Yaacov, Nathan Blake, Sander Beckers, Hana Chockler

diffusion safety

arXiv AI

Sep 15

To do($x$) or not to do($x$): Medical Image Counterfactuals for Dataset Augmentation

arXiv:2609.14124v1 Announce Type: cross Abstract: Medical image analysis is often hindered by biased datasets, which can lead to biased models and limited clinical applicability. A promising strategy...

By Yasin Ibrahim, Robin J. Evans, Konstantinos Kamnitsas

safety

Hugging Face Trending Papers

Sep 2

Generating Medical Image Counterfactuals using Causal Explanations

The paper introduces a new method for generating medical image counterfactuals that does not rely on auxiliary generative models. By extracting causal evidence directly from the classifier, the approach deterministically edits user-specified regions to alter predictions while staying closer to the original image than generative baselines. Experiments on real-world medical imaging datasets show that this technique provides a more direct and transparent view of the classifier’s decision boundary.

diffusion safety

arXiv Computer Vision

Sep 22

Generating Chest X-Ray Counterfactuals by Specialising Foundation Image Models

arXiv:2609.24879v1 Announce Type: new Abstract: Counterfactual image generation answers questions about how a subject would have looked under retrospective, hypothetical scenarios. Recent methods hav...

By Xiaodan Xing, Rajat R. Rasal, Julia A. Meister, Sara Ghorayeb, Galvin Khara, Jessica Schrouff

diffusion

arXiv Computer Vision

Aug 24

Counterfactual Stress Testing for Image Classification Models

arXiv:2605.10894v2 Announce Type: replace Abstract: Deep learning models in medical imaging often fail when deployed in new clinical environments due to distribution shifts in demographics, scanner h...

By Moritz Stammel, Fabio De Sousa Ribeiro, Raghav Mehta, M\'elanie Roschewitz, Ben Glocker

computer-vision

arXiv Machine Learning

Jun 16

Navigating Distribution Shifts in Medical Image Analysis: A Survey

arXiv:2411. 05824v4 Announce Type: replace-cross Abstract: Medical Image Analysis (MedIA) has become indispensable in modern healthcare, enhancing clinical diagnostics and personalized treatment.

By Zixian Su, Jingwei Guo, Xi Yang, Qiufeng Wang, Frans Coenen, Amir Hussain, Kaizhu Huang

fine-tuning safety

arXiv Computer Vision

Sep 1

Text-Guided Diffusion-Based Adversarial Attacks on Chest X-Ray Images

arXiv:2608.29456v1 Announce Type: new Abstract: As artificial intelligence is increasingly integrated into chest X-ray (CXR) interpretation, triage, and clinical decision support, understanding its v...

By Basudha Pal, Arjun Narayanan, Neha Ajith, Vikas R Bhat, Muhammad Umair

diffusion robotics safety

arXiv AI

Jul 2

Text Over Image: Auditing Multimodal Robustness in Synthetic Medical Image Detection

arXiv:2606. 25375v2 Announce Type: replace-cross Abstract: With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud.

By Ching-Hao Chiu, Hao-Wei Chung, Gelei Xu, Xueyang Li, Pin-Yu Chen, John Kheir, Meysam Ghaffari, Carlos Morato, Ahmed Abbasi, Yiyu Shi

llms multimodal benchmarks

arXiv Machine Learning

Jul 30

AI Alignment in Medical Imaging: Unveiling Hidden Biases Through Counterfactual Analysis

arXiv:2504. 19621v2 Announce Type: replace Abstract: Machine learning (ML) systems for medical imaging have demonstrated remarkable diagnostic capabilities, but their susceptibility to biases poses significant risks, since biases may negatively impact generalization performance.

By Haroui Ma, Francesco Quinzan, Theresa Willem, Stefan Bauer

diffusion safety

arXiv AI

Jul 1

Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines

arXiv:2605. 23995v4 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) is increasingly used in medical image analysis to reduce dependence on costly expert annotations by learning transferable representations from unlabeled data.

By Chathura Wimalasiri, Kishor Nandakishor, Marimuthu Palaniswami

computer-vision

arXiv Machine Learning

Jul 13

Secure-by-Disguise: A Systematic Evaluation of Image Disguising for Confidential Medical Image Modeling

arXiv:2607. 08867v1 Announce Type: cross Abstract: Cloud-based deep learning enables large-scale medical image analysis but raises significant privacy concerns when sensitive patient images are outsourced for model development.

By Jason Rojas, Jiajie He, Yash Patel, Yuechun Gu, Zeyun Yu, Keke Chen

computer-vision safety

arXiv AI

Jun 2

RL-ACRGNet: Reinforcement Learning-Based Chest Radiology Report Generation Network

arXiv:2606. 02035v1 Announce Type: new Abstract: Medical imaging interpretation is a foundational pillar of modern clinical diagnostics, yet the manual generation of radiology reports remains a time-consuming process prone to interpretation inconsistencies.

By Yogesh Kumar Meena, Saurabh Agarwal, K. V. Arya

rag reinforcement-learning benchmarks

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