MuSiC-ViT:一个多任务的姆卷积视觉转换器,用于区分随访胸部X射线图的变化与无变化
Kyungjin Cho1, Jeeyoung Kim1, Ki Duk Kim2
1Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Republic of Korea.
Medical image analysis
|August 10, 2023
概括
放射科医生在随访胸部X射线图 (CXR) 上识别变化时面临着挑战. 一个新的AI模型,MuSiC-ViT,模仿放射科医生的认知,以准确检测CXRs的变化,提高诊断准确度.
科学领域:
- 医疗成像中的人工智能
- 放射学 放射学是一门学科.
- 计算机视觉 计算机视觉
背景情况:
- 放射科医生经常解释后续的胸部X射线图 (CXR) 来监测患者的病情.
- 区分真正的疾病进展与良性CXR变异是一个重大的诊断挑战.
研究的目的:
- 开发和评估一种新的人工智能模型,MuSiC-ViT,旨在帮助放射科医生识别随访CXR中的有意义的变化.
- 模仿放射科医生在区分疾病变化和对CXRs无变化的认知过程.
主要方法:
- 开发了一种多任务的姆卷积视觉变压器 (MuSiC-ViT) 模型,集成了CNN和变压器.
- 该模型包含一个解剖匹配模块 (AMM),并使用多任务学习进行分类 (正常/异常,变化/不变化) 和解剖匹配训练.
- 使用了406K个CXR的大数据集,包括88K个变化对和115K个不变化的对用于训练.
主要成果:
- 在内部验证数据集上,MuSiC-ViT实现了0.728的精度和0.797的接收器操作特征曲线 (AUC) 下的面积.
- 外部验证数据集的性能为0.614准确度和0.784 AUC,第二个数据集为0.745准确度和0.858 AUC.
- 这些结果表明该模型在不同数据集的变化检测中具有强度和有效性.
结论:
- MuSiC-ViT模型显示出作为人工智能工具的承诺,以帮助放射科医生在解释后续CXR的挑战性任务中发挥作用.
- 该模型通过罗式架构和AMM模拟放射科医生的认知过程的能力,有助于其性能.
- 进一步验证和整合到临床工作流程中可以提高放射学诊断准确性和患者护理.
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