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...
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.
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.
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...
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.
arXiv:2606. 04009v1 Announce Type: cross Abstract: Two-sample testing is a fundamental tool for detecting distributional differences across scientific domains, but classical tests (including kernel-based tests) can be ineffective on high-dimensional structured data such as images.
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.
The paper introduces counterfactual (CF) marginalisation, a test‑time evaluation method that assesses how robust classification models are to nuisance variables such as age or sex. By using a CF image generator to intervene on these parent variables, the method creates counterfactual versions of each test image and averages predictions over a chosen intervention distribution, yielding intervention‑aware predictions that filter out demographic effects while retaining patient‑specific latent information. These predictions are then used to define metrics for CF risk, calibration, stability, and worst‑case sensitivity, demonstrating the framework’s usefulness for quantitative robustness evaluation.
The paper introduces a classifier‑free method for generating visual counterfactual explanations (VCEs) using Contrastive Analysis (CA). By separating generative factors common to two datasets from those specific to each class, the approach swaps only the salient factors to produce counterfactual images, thereby avoiding reliance on classifier decision boundaries. Leveraging StyleGAN2’s high‑quality synthesis and a feature‑space latent representation, the method supports multiple salient factors per dataset and achieves superior counterfactual quality on three medical imaging datasets.
arXiv:2607. 02596v1 Announce Type: cross Abstract: Deep learning models for medical diagnosis frequently exhibit substantial performance disparities across sensitive subgroups (e.
The paper presents a plug‑and‑play framework that uses concept-based interpretation to debug medical imaging models. By aligning a single‑modality encoder with BioMedCLIP, the authors build a Concept Bottleneck Model that allows concept‑level interventions to distinguish causal from spurious features. Experiments on Mayo Clinic ultrasound and CheXpert chest X‑ray data show that these interventions can reliably diagnose model issues and even improve performance through guided fine‑tuning.
The paper introduces a counterfactual data augmentation method that generates healthy mammograms by inpainting lesions with realistic normal tissue using a Denoising Diffusion Probabilistic Model and a RePaint-based sampling strategy. The generated healthy counterfactuals preserve patient-specific anatomy and match the quality of the original dataset, as confirmed by similarity metrics and radiologist assessment. When applied to four different classifier architectures on the VinDr-Mammo dataset, the augmentation improves sensitivity, especially at 80% fixed specificity, thereby enhancing the reliability of computer-aided breast cancer screening systems.