VisionCAD: An Integration-Free Radiology Copilot Framework
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908624371056640 |
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| author | Li, Jiaming Wu, Junlei Wang, Sheng Xiong, Honglin Cai, Jiangdong Zhao, Zihao Zhu, Yitao Yin, Yuan Shen, Dinggang Wang, Qian |
| author_facet | Li, Jiaming Wu, Junlei Wang, Sheng Xiong, Honglin Cai, Jiangdong Zhao, Zihao Zhu, Yitao Yin, Yuan Shen, Dinggang Wang, Qian |
| contents | Widespread clinical deployment of computer-aided diagnosis (CAD) systems is hindered by the challenge of integrating with existing hospital IT infrastructure. Here, we introduce VisionCAD, a vision-based radiological assistance framework that circumvents this barrier by capturing medical images directly from displays using a camera system. The framework operates through an automated pipeline that detects, restores, and analyzes on-screen medical images, transforming camera-captured visual data into diagnostic-quality images suitable for automated analysis and report generation. We validated VisionCAD across diverse medical imaging datasets, demonstrating that our modular architecture can flexibly utilize state-of-the-art diagnostic models for specific tasks. The system achieves diagnostic performance comparable to conventional CAD systems operating on original digital images, with an F1-score degradation typically less than 2\% across classification tasks, while natural language generation metrics for automated reports remain within 1\% of those derived from original images. By requiring only a camera device and standard computing resources, VisionCAD offers an accessible approach for AI-assisted diagnosis, enabling the deployment of diagnostic capabilities in diverse clinical settings without modifications to existing infrastructure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VisionCAD: An Integration-Free Radiology Copilot Framework Li, Jiaming Wu, Junlei Wang, Sheng Xiong, Honglin Cai, Jiangdong Zhao, Zihao Zhu, Yitao Yin, Yuan Shen, Dinggang Wang, Qian Computer Vision and Pattern Recognition Human-Computer Interaction Widespread clinical deployment of computer-aided diagnosis (CAD) systems is hindered by the challenge of integrating with existing hospital IT infrastructure. Here, we introduce VisionCAD, a vision-based radiological assistance framework that circumvents this barrier by capturing medical images directly from displays using a camera system. The framework operates through an automated pipeline that detects, restores, and analyzes on-screen medical images, transforming camera-captured visual data into diagnostic-quality images suitable for automated analysis and report generation. We validated VisionCAD across diverse medical imaging datasets, demonstrating that our modular architecture can flexibly utilize state-of-the-art diagnostic models for specific tasks. The system achieves diagnostic performance comparable to conventional CAD systems operating on original digital images, with an F1-score degradation typically less than 2\% across classification tasks, while natural language generation metrics for automated reports remain within 1\% of those derived from original images. By requiring only a camera device and standard computing resources, VisionCAD offers an accessible approach for AI-assisted diagnosis, enabling the deployment of diagnostic capabilities in diverse clinical settings without modifications to existing infrastructure. |
| title | VisionCAD: An Integration-Free Radiology Copilot Framework |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2511.00381 |