mlsanityData to 7 Oct 2026

Chest-Diffusion: A Light-Weight Text-to-Image Model for Report-to-CXR Generation

arXiv 2407.00752 ↗First posted 30 Jun 2024cs.CV cs.AIcited by 16

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Text-to-image generation has important implications for generation of diverse and controllable images. Several attempts have been made to adapt Stable Diffusion (SD) to the medical domain. However, the large distribution difference between medical reports and natural texts, as well as high computational complexity in common stable diffusion limit the authenticity and feasibility of the generated medical images. To solve above problems, we propose a novel light-weight transformer-based diffusion model learning framework, Chest-Diffusion, for report-to-CXR generation. Chest-Diffusion employs a domain-specific text encoder to obtain accurate and expressive text features to guide image generation, improving the authenticity of the generated images. Meanwhile, we introduce a light-weight transformer architecture as the denoising model, reducing the computational complexity of the diffusion model. Experiments demonstrate that our Chest-Diffusion achieves the lowest FID score 24.456, under the computation budget of 118.918 GFLOPs, which is nearly one-third of the computational complexity of SD.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 15 of its 24 references that are in the map. First posted on arXiv 30 Jun 2024.

Builds on

15 of 24 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains9 Oct

Other references · 14

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  5. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  6. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  7. 2021CogView: Mastering Text-to-Image Generation via Transformerscited by 691
  8. 2023Muse: Text-To-Image Generation via Masked Generative Transformerscited by 640
  9. 2019MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographscited by 590
  10. 2022All are Worth Words: A ViT Backbone for Diffusion Modelscited by 498
  11. 2021TorchXRayVision: A library of chest X-ray datasets and modelscited by 131
  12. 2022RoentGen: Vision-Language Foundation Model for Chest X-ray Generationcited by 89
2 more
  1. 2023LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generationcited by 57
  2. 2021Deep learning-based synthetic-CT generation in radiotherapy and PET: a reviewcited by 28

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 11; 2026: 4.

16 papers.

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  1. 2025Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedbackcited by 9
  2. 2025Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translationcited by 8
  3. 2025Prompt to Polyp: Medical Text-Conditioned Image Synthesis with Diffusion Modelscited by 5
  4. 2025CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographscited by 5
  5. 2025CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Modelscited by 3
  6. 2025Radiology Report Conditional 3D CT Generation with Multi Encoder Latent diffusion Modelcited by 3
  7. 2025VAP-Diffusion: Enriching Descriptions with MLLMs for Enhanced Medical Image Generationcited by 2
  8. 2025Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identificationcited by 2
  9. 2024Diff-CXR: Report-to-CXR generation through a disease-knowledge enhanced diffusion modelcited by 1
  10. 2025Accelerating Diffusion Sampling via Exploiting Local Transition Coherencecited by 1
  11. 2025Anatomy-Grounded Weakly Supervised Prompt Tuning for Chest X-ray Latent Diffusion Modelscited by 1
  12. 2026CRAFT: Clinical Reward-Aligned Finetuning for Medical Image Synthesiscited by 1
4 more
  1. 2026An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentationcited by 1
  2. 2025AURAD: Anatomy-Pathology Unified Radiology Synthesis with Progressive Representationscited by 0
  3. 20262D Versus 3D Diffusion for In Silico Training of Interventional X-ray AI Modelscited by 0
  4. 2026From Image Interpretation to Clinical Reasoning: Upstream Physician-Context-Aware Multimodal Learning with Causal Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion
  2. 2025Med-Art: Diffusion Transformer for 2D Medical Text-to-Image Generation
  3. 2024Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning
  4. 2022Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Models
  5. 2025Language-Guided Trajectory Traversal in Disentangled Stable Diffusion Latent Space for Factorized Medical Image Generation
  6. 2023Medical diffusion on a budget: Textual Inversion for medical image generation
  7. 2023Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis
  8. 2026UniX: Unifying Autoregression and Diffusion for Chest X-Ray Understanding and Generation