# CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning

arXiv: https://arxiv.org/abs/2504.13820 · HTML: https://mlsanity.com/paper/2504.13820/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Medical and surgical world models, one of 11 directions in World models.
- Builds on 52 of its 72 references that are in the map.
- First posted on arXiv 18 Apr 2025.

## Placed in

- World models / [Medical and surgical world models](https://mlsanity.com/wm/medical-and-surgical-world-models/)

## Builds on (most cited first, 30 of 52)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [Mastering Atari with Discrete World Models](https://mlsanity.com/paper/2010.02193/) (2020)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [WorldSimBench: Towards Video Generation Models as World Simulators](https://mlsanity.com/paper/2410.18072/) (2024)
- [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [Exploring Plain Vision Transformer Backbones for Object Detection](https://mlsanity.com/paper/2203.16527/) (2022)
- [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023)
- [VideoPoet: A Large Language Model for Zero-Shot Video Generation](https://mlsanity.com/paper/2312.14125/) (2023)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [A Learned Representation For Artistic Style](https://mlsanity.com/paper/1610.07629/) (2016)
- [Big Self-Supervised Models Advance Medical Image Classification](https://mlsanity.com/paper/2101.05224/) (2021)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations](https://mlsanity.com/paper/2012.15029/) (2020)

## Built on by (most cited first, 19 of 19)

- [Efficient Chest X-ray Representation Learning via Semantic-Partitioned Contrastive Learning](https://mlsanity.com/paper/2603.07113/) (2026)
- [Beyond Generative AI: World Models for Clinical Prediction, Counterfactuals, and Planning](https://mlsanity.com/paper/2511.16333/) (2025)
- [Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies](https://mlsanity.com/paper/2606.16721/) (2026)
- [Medical world models in healthcare: foundations, applications, and challenges for trustworthy clinical translation](https://mlsanity.com/paper/2607.25242/) (2026)
- [VasoMIM: Vascular Anatomy-Aware Masked Image Modeling for Vessel Segmentation](https://mlsanity.com/paper/2508.10794/) (2025)
- [X-WIN: Building Chest Radiograph World Model via Predictive Sensing](https://mlsanity.com/paper/2511.14918/) (2025)
- [Beyond Patient Invariance: Learning Cardiac Dynamics via Action-Conditioned JEPAs](https://mlsanity.com/paper/2604.22618/) (2026)
- [A World Model of Radiologist Reading for Medical Image Representation Learning](https://mlsanity.com/paper/2605.23992/) (2026)
- [VA-Adapter: Adapting Ultrasound Foundation Model to Echocardiography Probe Guidance](https://mlsanity.com/paper/2510.06809/) (2025)
- [RadJEPA: Radiology Encoder for Chest X-Rays via Joint Embedding Predictive Architecture](https://mlsanity.com/paper/2601.15891/) (2026)
- [Seeing Like Radiologists: Context- and Gaze-Guided Vision-Language Pretraining for Chest X-rays](https://mlsanity.com/paper/2603.26049/) (2026)
- [SliceWorld: A Predictive and Controllable World-State Model for CT Report Generation](https://mlsanity.com/paper/2605.24371/) (2026)
- [Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology](https://mlsanity.com/paper/2608.13518/) (2026)
- [Vascular anatomy-aware self-supervised pre-training for X-ray angiogram analysis](https://mlsanity.com/paper/2602.11536/) (2026)
- [UltraStar: Semantic-Aware Star Graph Modeling for Echocardiography Navigation](https://mlsanity.com/paper/2603.01461/) (2026)
- [3D MRI Image Pretraining via Controllable 2D Slice Navigation Task](https://mlsanity.com/paper/2605.06487/) (2026)
- [AuricularWorld: Hierarchical Action-Guided World Modeling for Fine-Grained Auricular Structure Segmentation from CT Scans](https://mlsanity.com/paper/2607.28487/) (2026)
- [HounsWorld: A Multimodal World Model for Hidden Patient-State Readout, Reconstruction, and Simulation](https://mlsanity.com/paper/2608.12904/) (2026)
- [The Predictability Bias of Joint-Embedding Predictive Learning in Medical Imaging](https://mlsanity.com/paper/2610.13237/) (2026)

## Abstract (arXiv)

Humans can develop internal world models that encode common sense knowledge, telling them how the world works and predicting the consequences of their actions. This concept has emerged as a promising direction for establishing general-purpose machine-learning models in recent preliminary works, e.g., for visual representation learning. In this paper, we present CheXWorld, the first effort towards a self-supervised world model for radiographic images. Specifically, our work develops a unified framework that simultaneously models three aspects of medical knowledge essential for qualified radiologists, including 1) local anatomical structures describing the fine-grained characteristics of local tissues (e.g., architectures, shapes, and textures); 2) global anatomical layouts describing the global organization of the human body (e.g., layouts of organs and skeletons); and 3) domain variations that encourage CheXWorld to model the transitions across different appearance domains of radiographs (e.g., varying clarity, contrast, and exposure caused by collecting radiographs from different hospitals, devices, or patients). Empirically, we design tailored qualitative and quantitative analyses, revealing that CheXWorld successfully captures these three dimensions of medical knowledge. Furthermore, transfer learning experiments across eight medical image classification and segmentation benchmarks showcase that CheXWorld significantly outperforms existing SSL methods and large-scale medical foundation models. Code & pre-trained models are available at https://github.com/LeapLabTHU/CheXWorld.