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Timestep-conditioned Attention and Multi-dimensional Evidence framework for efficient multimodal chest X-ray anomaly
Xueyu Kang1, Qiulan Liu2, Hailing Feng1
1Department of Computer Science, Shandong Xiehe University, Jinan, 250109, Shandong, China.
Scientific Reports
|July 7, 2026
Summary
This study introduces TAME for efficient unsupervised anomaly detection in chest X-rays, using lightweight models and multi-dimensional evidence fusion for improved accuracy and resource efficiency in medical imaging.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Healthcare
- Radiology
Background:
- Unsupervised anomaly detection (UAD) in chest X-rays identifies pathologies without abnormal labels.
- EHR-conditioned diffusion models show promise but are computationally intensive.
- Existing methods often miss diagnostic evidence from intermediate steps.
Purpose of the Study:
- To develop a resource-efficient UAD framework for chest X-rays.
- To address limitations of heavy models and incomplete anomaly evidence utilization.
- To improve accuracy and practical deployment in medical environments.
Main Methods:
- Proposed Timestep-conditioned Attention for Multi-dimensional Evidence (TAME) framework.
- Introduced Timestep-Conditioned Channel Attention (TCCA) for efficient, lightweight model training.
- Developed Multi-Dimensional Anomaly Evidence Fusion (MDAEF) for comprehensive anomaly scoring.
Main Results:
- TAME achieved accurate and resource-efficient medical anomaly detection on CheXpert and MIMIC-CXR datasets.
- The lightweight 96-channel TCCA module enabled efficient training.
- MDAEF successfully aggregated multi-dimensional evidence for enhanced detection.
Conclusions:
- TAME offers a unified, accurate, and efficient solution for UAD in chest X-rays.
- TCCA and MDAEF components demonstrate complementary benefits for practical medical AI.
- The framework enhances diagnostic capabilities in resource-constrained settings.