Generating Chest X-Ray Counterfactuals by Specialising Foundation Image Models
cs.CV
Submitted: 2026-09-21
Updated: 2026-09-21
Code: https://github.com/GSK-AI/RadCF
Terminology
Sources
- Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
- BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys
- Counterfactual Fairness
- REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers
- SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
- Benchmarking Counterfactual Image Generation
- Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
- DINOv2: Learning Robust Visual Features without Supervision
- Scalable Diffusion Models with Transformers
- Generative AI for Medical Imaging: extending the MONAI Framework
- SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
- CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
- Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation
- Factored Classifier-Free Guidance
- Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think
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