ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion
Chest X-ray report generation (CXR-RG) has the potential to substantially alleviate radiologists' workload. However, conventional autoregressive vision--language models (VLMs) suffer from high inference latency due to sequential token decoding. Diffusion-based models offer a promising alternative through parallel generation, but they still require multiple denoising iterations. Compressing multi-step denoising to a single step could further reduce latency, but often degrades textual coherence due to the mean-field bias introduced by token-factorized denoisers. To address this challenge, we propose ECHO, an efficient diffusion-based VLM (dVLM) for chest X-ray report generation. ECHO enables stable one-step-per-block inference via a novel Direct Conditional Distillation (DCD) framework, which mitigates the mean-field limitation by constructing unfactorized supervision from on-policy diffusion trajectories to encode joint token dependencies. In addition, we introduce a Response-Asymmetric Diffusion (RAD) training strategy that further improves training efficiency while maintaining model effectiveness. Extensive experiments demonstrate that ECHO surpasses state-of-the-art autoregressive methods, improving RaTE and SemScore by 64.33\% and 60.58\% respectively, while achieving an 8times inference speedup without compromising clinical accuracy.
69dc5eaa2b6b8dc66ffeebf9
Lifeng Chen
claimed_verified
2026-04-13T07:32:06.716Z
66939fec286045211df7bbd7
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/7HPeUeZd5KIUwa6Z8oGXE.png
Chen Lifeng
Constant8868
user
Constant8868
69dc5eaa2b6b8dc66ffeebfa
Tianqi You
69dc5eaa2b6b8dc66ffeebfb
Hao Liu
69dc5eaa2b6b8dc66ffeebfc
Zhimin Bao
69dc5eaa2b6b8dc66ffeebfd
Jile Jiao
69dc5eaa2b6b8dc66ffeebfe
Xiao Han
69dc5eaa2b6b8dc66ffeebff
Zhicai Ou
69dc5eaa2b6b8dc66ffeec00
Tao Sun
69dc5eaa2b6b8dc66ffeec01
Xiaofeng Mou
69dc5eaa2b6b8dc66ffeec02
Xiaojie Jin
69dc5eaa2b6b8dc66ffeec03
Yi Xu
ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion
Chest X-ray report generation (CXR-RG) has the potential to substantially alleviate radiologists' workload. However, conventional autoregressive vision--language models (VLMs) suffer from high inference latency due to sequential token decoding. Diffusion-based models offer a promising alternative through parallel generation, but they still require multiple denoising iterations. Compressing multi-step denoising to a single step could further reduce latency, but often degrades textual coherence due to the mean-field bias introduced by token-factorized denoisers. To address this challenge, we propose ECHO, an efficient diffusion-based VLM (dVLM) for chest X-ray report generation. ECHO enables stable one-step-per-block inference via a novel Direct Conditional Distillation (DCD) framework, which mitigates the mean-field limitation by constructing unfactorized supervision from on-policy diffusion trajectories to encode joint token dependencies. In addition, we introduce a Response-Asymmetric Diffusion (RAD) training strategy that further improves training efficiency while maintaining model effectiveness. Extensive experiments demonstrate that ECHO surpasses state-of-the-art autoregressive methods, improving RaTE and SemScore by 64.33\% and 60.58\% respectively, while achieving an 8times inference speedup without compromising clinical accuracy.
ECHO is an efficient diffusion-based vision-language model for chest X-ray report generation that achieves faster inference through direct conditional distillation and response-asymmetric diffusion training while maintaining high clinical accuracy.
vision--language models diffusion-based models autoregressive models denoising iterations mean-field bias token-factorized denoisers Direct Conditional Distillation Response-Asymmetric Diffusion on-policy diffusion trajectories joint token dependencies inference speedup clinical accuracy