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arXiv · 2312.16151

Large-scale Long-tailed Disease Diagnosis on Radiology Images

Qiaoyu Zheng·Weike Zhao·Chaoyi Wu·Xiaoman Zhang·Lisong Dai·Hengyu Guan·Yuehua Li·Ya Zhang·Yanfeng Wang·Weidi Xie
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Abstract

Developing a generalist radiology diagnosis system can greatly enhance clinical diagnostics. In this paper, we introduce RadDiag, a foundational model supporting 2D and 3D inputs across various modalities and anatomies, using a transformer-based fusion module for comprehensive disease diagnosis. Due to patient privacy concerns and the lack of large-scale radiology diagnosis datasets, we utilize high-quality, clinician-reviewed radiological images available online with diagnosis labels. Our dataset, RP3D-DiagDS, contains 40,936 cases with 195,010 scans covering 5,568 disorders (930 unique ICD-10-CM codes). Experimentally, our RadDiag achieves 95.14% AUC on internal evaluation with the knowledge-enhancement strategy. Additionally, RadDiag can be zero-shot applied or fine-tuned to external diagnosis datasets sourced from various hospitals, demonstrating state-of-the-art results. In conclusion, we show that publicly shared medical data on the Internet is a tremendous and valuable resource that can potentially support building a generalist AI for healthcare.

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Qiaoyu Zheng, Weike Zhao, Chaoyi Wu, Xiaoman Zhang, Lisong Dai, Hengyu Guan, Yuehua Li, Ya Zhang, Yanfeng Wang, Weidi Xie. 2023-12-26. Large-scale Long-tailed Disease Diagnosis on Radiology Images. https://arxiv.org/abs/2312.16151

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