mlsanityData to 7 Oct 2026

A Structural Causal Model for MR Images of Multiple Sclerosis

arXiv 2103.03158 ↗First posted 4 Mar 2021cs.CV cs.LG eess.IVcited by 21

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Abstract (arXiv)

Precision medicine involves answering counterfactual questions such as "Would this patient respond better to treatment A or treatment B?" These types of questions are causal in nature and require the tools of causal inference to be answered, e.g., with a structural causal model (SCM). In this work, we develop an SCM that models the interaction between demographic information, disease covariates, and magnetic resonance (MR) images of the brain for people with multiple sclerosis. Inference in the SCM generates counterfactual images that show what an MR image of the brain would look like if demographic or disease covariates are changed. These images can be used for modeling disease progression or used for image processing tasks where controlling for confounders is necessary.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 23 of its 48 references that are in the map. First posted on arXiv 4 Mar 2021.

Builds on

23 of 48 references.

References · 23

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 8; 2023: 5; 2024: 3; 2025: 2; 2026: 2.

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  1. 2022Causal Machine Learning for Healthcare and Precision Medicinecited by 68
  2. 2021Learning Disentangled Representations in the Imaging Domaincited by 46
  3. 2022Counterfactual harmcited by 29
  4. 2023RadEdit: stress-testing biomedical vision models via diffusion image editingcited by 28
  5. 2022A Review of Causality for Learning Algorithms in Medical Image Analysiscited by 18
  6. 2022CaRTS: Causality-driven Robot Tool Segmentation from Vision and Kinematics Datacited by 16
  7. 2022Deep Structural Causal Shape Modelscited by 11
  8. 2022Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markerscited by 10
  9. 2023Emerging Synergies in Causality and Deep Generative Models: A Surveycited by 10
  10. 2025Diffusion Counterfactual Generation with Semantic Abductioncited by 8
  11. 2023LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentationcited by 7
  12. 2022Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Imagescited by 5
9 more
  1. 2022Rethinking Causality-driven Robot Tool Segmentation with Temporal Constraintscited by 5
  2. 2024Applications of interpretable deep learning in neuroimaging: a comprehensive reviewcited by 3
  3. 2023Punctate White Matter Lesion Segmentation in Preterm Infants Powered by Counterfactually Generative Learningcited by 2
  4. 2024SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentationcited by 1
  5. 2025RoentMod: A Synthetic Chest X-Ray Modification Model to Identify and Correct Image Interpretation Model Shortcutscited by 1
  6. 2026Radiologist-Guided Causal Concept Bottleneck Models for Chest X-Ray Interpretationcited by 1
  7. 2023Causal Image Synthesis of Brain MR in 3Dcited by 0
  8. 2024Longitudinal Causal Image Synthesiscited by 0
  9. 2026Geodesic-informed Generative Diffusion Model For Topology-preserved Image Video Generationcited by 0