# PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion

arXiv: https://arxiv.org/abs/2503.00196 · HTML: https://mlsanity.com/paper/2503.00196/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 27 of its 34 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 27 of 27)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [BCN20000: Dermoscopic Lesions in the Wild](https://mlsanity.com/paper/1908.02288/) (2019)
- [Finetune like you pretrain: Improved finetuning of zero-shot vision models](https://mlsanity.com/paper/2212.00638/) (2022)
- [Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images](https://mlsanity.com/paper/2302.14042/) (2023)
- [Explanation by Progressive Exaggeration](https://mlsanity.com/paper/1911.00483/) (2019)
- [Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision](https://mlsanity.com/paper/2310.18689/) (2023)
- [Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity](https://mlsanity.com/paper/2305.08252/) (2023)
- [High Fidelity Image Counterfactuals with Probabilistic Causal Models](https://mlsanity.com/paper/2306.15764/) (2023)
- [GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learning](https://mlsanity.com/paper/2012.11905/) (2020)
- [LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images](https://mlsanity.com/paper/2305.19164/) (2023)
- [MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification](https://mlsanity.com/paper/2306.09579/) (2023)
- [CounteRGAN: Generating Realistic Counterfactuals with Residual Generative Adversarial Nets](https://mlsanity.com/paper/2009.05199/) (2020)
- [BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys](https://mlsanity.com/paper/2310.10765/) (2023)
- [Debiasing Counterfactuals In the Presence of Spurious Correlations](https://mlsanity.com/paper/2308.10984/) (2023)
- [Low-resource finetuning of foundation models beats state-of-the-art in histopathology](https://mlsanity.com/paper/2401.04720/) (2024)
- [DeViDe: Faceted medical knowledge for improved medical vision-language pre-training](https://mlsanity.com/paper/2404.03618/) (2024)
- [DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations](https://mlsanity.com/paper/2405.09288/) (2024)

## Built on by (most cited first, 17 of 17)

- [Counterfactual Identifiability via Dynamic Optimal Transport](https://mlsanity.com/paper/2510.08294/) (2025)
- [RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models](https://mlsanity.com/paper/2503.15784/) (2025)
- [Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation](https://mlsanity.com/paper/2510.22107/) (2025)
- [Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers](https://mlsanity.com/paper/2606.19460/) (2026)
- [Leveraging Vision-Language Foundation Models to Reveal Hidden Image-Attribute Relationships in Medical Imaging](https://mlsanity.com/paper/2503.23618/) (2025)
- [Anatomy-Grounded Weakly Supervised Prompt Tuning for Chest X-ray Latent Diffusion Models](https://mlsanity.com/paper/2506.10633/) (2025)
- [RoentMod: A Synthetic Chest X-Ray Modification Model to Identify and Correct Image Interpretation Model Shortcuts](https://mlsanity.com/paper/2509.08640/) (2025)
- [Med-Banana: Learning Quality-Controlled Medical Image Editing from Success-and-Failure Trajectories](https://mlsanity.com/paper/2511.00801/) (2025)
- [When the Edit Changes the Patient: Measuring Identity Preservation in Counterfactual Retinal Images](https://mlsanity.com/paper/2608.23024/) (2026)
- [Generating Chest X-Ray Counterfactuals by Specialising Foundation Image Models](https://mlsanity.com/paper/2609.24879/) (2026)
- [Language-Guided Trajectory Traversal in Disentangled Stable Diffusion Latent Space for Factorized Medical Image Generation](https://mlsanity.com/paper/2503.23623/) (2025)
- [Pixel Perfect MegaMed: A Megapixel-Scale Vision-Language Foundation Model for Generating High Resolution Medical Images](https://mlsanity.com/paper/2507.12698/) (2025)
- [MedSyn2: Flexible Control of 3D CT Generation via Text and Semantically-Defined Segmentation Prompts](https://mlsanity.com/paper/2606.00967/) (2026)
- [RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization](https://mlsanity.com/paper/2607.10777/) (2026)
- [Symbal: Detecting Systematic Misalignments in Model-Generated Captions](https://mlsanity.com/paper/2607.15216/) (2026)
- [FiRe: Fixed-Noise Refinement for Visual Counterfactual Explanations](https://mlsanity.com/paper/2608.08664/) (2026)
- [Spatially Gated Diffusion for Localized Counterfactual Chest Radiograph Editing](https://mlsanity.com/paper/2610.00805/) (2026)

## Abstract (arXiv)

Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, data imbalances, and limited text annotations in datasets. Addressing these challenges requires architectures that are robust to the unique complexities posed by medical imaging data. Rapid advancements in vision-language foundation models within the natural image domain prompt the question of how they can be adapted for medical imaging tasks. In this work, we present PRISM, a framework that leverages foundation models to generate high-resolution, language-guided medical image counterfactuals using Stable Diffusion. Our approach demonstrates unprecedented precision in selectively modifying spurious correlations (the medical devices) and disease features, enabling the removal and addition of specific attributes while preserving other image characteristics. Through extensive evaluation, we show how PRISM advances counterfactual generation and enables the development of more robust downstream classifiers for clinically deployable solutions. To facilitate broader adoption and research, we make our code publicly available at https://github.com/Amarkr1/PRISM.