UniX: Unifying Autoregression and Diffusion for Chest X-Ray Understanding and Generation
Despite recent progress, medical foundation models still struggle to unify visual understanding and generation, as these tasks have inherently conflicting goals: semantic abstraction versus pixel-level reconstruction. Existing approaches, typically based on parameter-shared autoregressive architectures, frequently lead to compromised performance in one or both tasks. To address this, we present UniX, a next-generation unified medical foundation model for chest X-ray understanding and generation. UniX decouples the two tasks into an autoregressive branch for understanding and a diffusion branch for high-fidelity generation. Crucially, a cross-modal self-attention mechanism is introduced to dynamically guide the generation process with understanding features. Coupled with a rigorous data cleaning pipeline and a multi-stage training strategy, this architecture enables synergistic collaboration between tasks while leveraging the strengths of diffusion models for superior generation. On two representative benchmarks, UniX achieves a 46.1% improvement in understanding performance (Micro-F1) and a 24.2% gain in generation quality (FD-RadDino), using only a quarter of the parameters of LLM-CXR. By achieving performance on par with task-specific models, our work establishes a scalable paradigm for synergistic medical image understanding and generation. Codes and models are available at https://github.com/ZrH42/UniX.
696f604fbfeda8d7e160c9a9
Ruiheng Zhang
claimed_verified
2026-01-20T14:48:05.051Z
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ruihengzhang
ZrH42
user
ZrH42
696f604fbfeda8d7e160c9aa
Jingfeng Yao
admin_assigned
2026-01-21T12:00:32.366Z
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Jingfeng Yao
MapleF9
user
MapleF9
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Huangxuan Zhao
696f604fbfeda8d7e160c9ac
Hao Yan
696f604fbfeda8d7e160c9ad
Xiao He
696f604fbfeda8d7e160c9ae
Lei Chen
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Zhou Wei
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Yong Luo
696f604fbfeda8d7e160c9b1
Zengmao Wang
696f604fbfeda8d7e160c9b2
Lefei Zhang
696f604fbfeda8d7e160c9b3
Dacheng Tao
696f604fbfeda8d7e160c9b4
Bo Du
UniX: Unifying Autoregression and Diffusion for Chest X-Ray Understanding and Generation
Despite recent progress, medical foundation models still struggle to unify visual understanding and generation, as these tasks have inherently conflicting goals: semantic abstraction versus pixel-level reconstruction. Existing approaches, typically based on parameter-shared autoregressive architectures, frequently lead to compromised performance in one or both tasks. To address this, we present UniX, a next-generation unified medical foundation model for chest X-ray understanding and generation. UniX decouples the two tasks into an autoregressive branch for understanding and a diffusion branch for high-fidelity generation. Crucially, a cross-modal self-attention mechanism is introduced to dynamically guide the generation process with understanding features. Coupled with a rigorous data cleaning pipeline and a multi-stage training strategy, this architecture enables synergistic collaboration between tasks while leveraging the strengths of diffusion models for superior generation. On two representative benchmarks, UniX achieves a 46.1% improvement in understanding performance (Micro-F1) and a 24.2% gain in generation quality (FD-RadDino), using only a quarter of the parameters of LLM-CXR. By achieving performance on par with task-specific models, our work establishes a scalable paradigm for synergistic medical image understanding and generation. Codes and models are available at https://github.com/ZrH42/UniX.
UniX presents a unified medical foundation model that decouples visual understanding and generation tasks using distinct autoregressive and diffusion branches with cross-modal attention for enhanced performance.
medical foundation models autoregressive architectures diffusion models cross-modal self-attention data cleaning pipeline multi-stage training strategy visual understanding image generation