title: CheXWorld: Exploring Image World Modeling for Radiograph Representation
    Learning
summary: 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.
paper_authors._id: 6805ab2c40034a5a792a26b2
paper_authors.name: Yang Yue
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-22T09:59:59.547Z
paper_authors._id: 63f1d16fbe95ed4c9a9418fe
paper_authors.avatarUrl: /avatars/a1bdfa97323693808f2f16ec74698ed3.svg
paper_authors.fullname: Yang Yue
paper_authors.type: user
paper_authors.user: yueyang2000
paper_authors._id: 6805ab2c40034a5a792a26b3
paper_authors.name: Yulin Wang
paper_authors._id: 6805ab2c40034a5a792a26b4
paper_authors.name: Chenxin Tao
paper_authors._id: 6805ab2c40034a5a792a26b5
paper_authors.name: Pan Liu
paper_authors._id: 6805ab2c40034a5a792a26b6
paper_authors.name: Shiji Song
paper_authors._id: 6805ab2c40034a5a792a26b7
paper_authors.name: Gao Huang
paper_title: CheXWorld: Exploring Image World Modeling for Radiograph Representation
    Learning
paper_summary: 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.
paper_ai_summary: CheXWorld is a self-supervised world model for radiographic images that captures local and global anatomical structures and domain variations, demonstrating superior performance in medical image tasks through transfer learning.
paper_ai_keywords: self-supervised world model medical knowledge local anatomical structures global anatomical layouts domain variations transfer learning medical image classification medical image segmentation SSL methods medical foundation models