Generating Medical Image Counterfactuals using Causal Explanations

• URL: http://arxiv.org/abs/2609.02697v1
• Date: Wed, 02 Sep 2026 15:03:42 GMT
• Title: Generating Medical Image Counterfactuals using Causal Explanations
• Abstract summary: We use counterfactual images to show how an image would need to change to produce a different prediction.<n>We propose a novel counterfactual-generation framework that requires no generative model.<n>The resulting approach is deterministic, requires no additional model training, and enables controllable edits within user-specified regions of interest.
• Score: 7.128196845242498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning models have achieved impressive performance in medical image diagnosis, yet their deployment in clinical settings remains constrained by limited explainability. Counterfactual images provide one means of auditing model behavior by showing how an image would need to change for a classifier to produce a different prediction. Existing approaches typically generate such explanations using auxiliary models, including generative adversarial networks and diffusion models. While often capable of producing visually realistic images, these methods explain one black-box model using another, making it difficult to separate the classifier's decision-making process from the inductive biases of the generator. We propose a novel counterfactual-generation framework that requires no generative model. Instead, counterfactuals are constructed directly from causal evidence extracted from the classifier. The resulting approach is deterministic, requires no additional model training, and enables controllable edits within user-specified regions of interest. Experiments on real-world medical imaging datasets demonstrate that the proposed method successfully changes classifier predictions while remaining closer to the original image than generative baselines, providing a more direct and transparent view of the classifier's decision boundary.

Related papers

• Detecting Generated Images by Fitting Natural Image Distributions [75.31113784234877]
  We propose a novel framework that exploits geometric differences between the data manifold of natural and generated images.<n>We employ a pair of functions engineered to yield consistent outputs for natural images but divergent outputs for generated ones.<n>An image is identified as generated if a transformation along its data manifold induces a significant change in the loss value of a self-supervised model pre-trained on natural images.
  arXiv  Detail & Related papers  (2025-11-03T07:20:38Z)
• Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis [55.959002385347645]
  Latent Drifting enables diffusion models to be conditioned for medical images fitted for the complex task of counterfactual image generation.<n>We evaluate our method on three public longitudinal benchmark datasets of brain MRI and chest X-rays for counterfactual image generation.
  arXiv  Detail & Related papers  (2024-12-30T01:59:34Z)
• Epistemic Uncertainty for Generated Image Detection [107.62647907393377]
  We introduce a novel framework for AI-generated image detection through epistemic uncertainty, aiming to address critical security concerns in the era of generative models.<n>Our key insight stems from the observation that distributional discrepancies between training and testing data manifest distinctively in the epistemic uncertainty space of machine learning models.
  arXiv  Detail & Related papers  (2024-12-08T11:32:25Z)
• Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder [38.81441978142279]
  We propose a novel method that operates directly on the latent space of a generative model, specifically a Diffusion Autoencoder (DAE) This approach offers inherent interpretability by enabling the generation of Counterfactual explanations (CEs) We show that these latent representations are helpful for medical condition classification and the ordinal regression of pathologies, such as vertebral compression fractures (VCF) and diabetic retinopathy (DR)
  arXiv  Detail & Related papers  (2024-08-02T21:01:30Z)
• Mask-guided cross-image attention for zero-shot in-silico histopathologic image generation with a diffusion model [0.10910416614141322]
  Diffusion models are the state-of-the-art solution for generating in-silico images.<n>Appearance transfer diffusion models are designed for natural images.<n>In computational pathology, specifically in oncology, it is not straightforward to define which objects in an image should be classified as foreground and background.<n>We contribute to the applicability of appearance transfer models to diffusion-stained images by modifying the appearance transfer guidance to alternate between class-specific AdaIN feature statistics matchings.
  arXiv  Detail & Related papers  (2024-07-16T12:36:26Z)
• Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation [7.7545714516743045]
  We propose an end-to-end framework called VerseDiff-UNet, which leverages the denoising diffusion probabilistic model (DDPM) Our approach integrates the diffusion model into a standard U-shaped architecture. We evaluate our method on a single dataset of spine images acquired through X-ray imaging.
  arXiv  Detail & Related papers  (2023-09-12T03:05:00Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• Your Diffusion Model is Secretly a Zero-Shot Classifier [90.40799216880342]
  We show that density estimates from large-scale text-to-image diffusion models can be leveraged to perform zero-shot classification. Our generative approach to classification attains strong results on a variety of benchmarks. Our results are a step toward using generative over discriminative models for downstream tasks.
  arXiv  Detail & Related papers  (2023-03-28T17:59:56Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• Generative Adversarial U-Net for Domain-free Medical Image Augmentation [49.72048151146307]
  The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. In this paper, we develop a novel generative method named generative adversarial U-Net. Our newly designed model is domain-free and generalizable to various medical images.
  arXiv  Detail & Related papers  (2021-01-12T23:02:26Z)
• Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification [0.0]
  We leverage a learning framework to produce our visual explanations method. Using metrics from the literature, our method outperforms state-of-the-art approaches. We validate our approach on a large chest X-ray database.
  arXiv  Detail & Related papers  (2020-12-14T08:34:12Z)
• A Question-Centric Model for Visual Question Answering in Medical Imaging [3.619444603816032]
  We present a novel Visual Question Answering approach that allows an image to be queried by means of a written question. Experiments on a variety of medical and natural image datasets show that by fusing image and question features in a novel way, the proposed approach achieves an equal or higher accuracy compared to current methods.
  arXiv  Detail & Related papers  (2020-03-02T10:16:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.