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Foundation models have been successful in natural language processing and computer vision because they are capable of capturing the underlying structures (foundation) of natural languages. However, in medical imaging, the key foundation lies in human anatomy, as these images directly represent the internal structures of the body, reflecting the consistency, coherence, and hierarchy of human anatomy. Yet, existing self-supervised learning (SSL) methods often overlook these perspectives, limiting their ability to effectively learn anatomical features. To overcome the limitation, we built Lamps (learning anatomy from multiple perspectives via self-supervision) pre-trained on large-scale chest radiographs by harmoniously utilizing the consistency, coherence, and hierarchy of human anatomy as the supervision signal. Extensive experiments across 10 datasets–evaluated through fine-tuning and emergent property analysis–demonstrate Lamps’ superior robustness, transferability, and clinical potential when compared to 10 baseline models. By learning from multiple perspectives, Lamps presents a unique opportunity for foundation models to develop meaningful, robust representations that are aligned with the structure of human anatomy. All code and pretrained models are publicly released ( GitHub.com/JLiangLab/Lamps ).

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Three stylized light bulb illustrations are arranged in a row. The central bulb is yellow, while the two outer bulbs are dark blue. Each bulb is surrounded by small lines, suggesting illumination. The design is simple and uses a minimal color palette. Lamps: Learning Anatomy from Multiple Perspectives via Self-supervision in Chest Radiographs

  • 会议论文

  • 发表日期：

    2026年

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  • Ziyu Zhou,
  • Haozhe Luo,
  • Mohammad Reza Hosseinzadeh Taher,
  • Jiaxuan Pang,
  • Xiaowei Ding,
  • Michael B. Gotway,
  • Jianming Liang

摘要

Foundation models have been successful in natural language processing and computer vision because they are capable of capturing the underlying structures (foundation) of natural languages. However, in medical imaging, the key foundation lies in human anatomy, as these images directly represent the internal structures of the body, reflecting the consistency, coherence, and hierarchy of human anatomy. Yet, existing self-supervised learning (SSL) methods often overlook these perspectives, limiting their ability to effectively learn anatomical features. To overcome the limitation, we built Lamps (learning anatomy from multiple perspectives via self-supervision) pre-trained on large-scale chest radiographs by harmoniously utilizing the consistency, coherence, and hierarchy of human anatomy as the supervision signal. Extensive experiments across 10 datasets–evaluated through fine-tuning and emergent property analysis–demonstrate Lamps’ superior robustness, transferability, and clinical potential when compared to 10 baseline models. By learning from multiple perspectives, Lamps presents a unique opportunity for foundation models to develop meaningful, robust representations that are aligned with the structure of human anatomy. All code and pretrained models are publicly released ( GitHub.com/JLiangLab/Lamps ).