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AnyCXR framework enables generalizable chest X-ray anatomy segmentation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed AnyCXR, a novel framework for segmenting human anatomy in chest X-rays across various acquisition positions. This system utilizes a large dataset of synthetic radiographs generated through Multi-stage Domain Randomization and a Conditional Joint Annotation Regularization learning strategy to handle imperfect annotations. AnyCXR demonstrates strong zero-shot generalization on real-world data, accurately identifying 54 anatomical structures and supporting downstream clinical tasks like cardiothoracic ratio estimation and disease classification. AI

IMPACT This framework could significantly reduce annotation burdens in medical imaging, leading to more robust and scalable AI-driven diagnostic tools.

RANK_REASON The cluster describes a research paper published on arXiv detailing a new technical framework for medical image analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AnyCXR framework enables generalizable chest X-ray anatomy segmentation

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The cluster describes a research paper published on arXiv detailing a new technical framework for medical image analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Zifei Dong, Wenjie Wu, Jinkui Hao, Tianqi Chen, Ziqiao Weng, Bo Zhou ·

    AnyCXR: Human Anatomy Segmentation of Chest X-ray at Any Acquisition Position using Multi-stage Domain Randomized Synthetic Data with Imperfect Annotations and Conditional Joint Annotation Regularization Learning

    arXiv:2512.17263v3 Announce Type: replace Abstract: Robust anatomical segmentation of chest X-rays (CXRs) remains challenging due to the scarcity of comprehensive annotations and the substantial variability of real-world acquisition conditions. We propose AnyCXR, a unified framew…

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