十字形窗户变压器与自我监督的预训,用于在双参数MRI中检测临床上显著的前列腺癌
Yuheng Li1,2, Jacob Wynne1, Jing Wang1
1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
Medical physics
|November 26, 2024
概括
一个新的自主监督学习框架与CSwin UNet增强使用双参数MRI的前列腺癌检测. 这种变压器模型提高了诊断准确性和通用性,优于现有方法.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 在瘤学瘤学.
背景情况:
- 双参数磁共振成像 (bpMRI) 显示出前列腺癌 (PCa) 检测的前景.
- 视觉变压器提供竞争性性能,但需要大量的注释数据.
- 自主监督学习 (SSL) 有效地利用未标记的数据进行特征提取.
研究的目的:
- 引入一种新的SSL框架和变压器模型,用于提高前列腺bpMRI中的PCa检测.
- 提高PCa诊断的深度学习模型的可通用性和数据效率.
主要方法:
- 开发了一个端到端的交叉形窗口 (CSwin) 变压器UNet (CSwin UNet) 用于检测临床显著的PCa (csPCa).
- 实施了多任务SSL框架,用于在大型bpmri数据集 (PI-CAI) 上预训练CSwin变压器.
- 使用病变注释微调模型,并在单独的数据集 (前列腺158) 上验证其概括.
主要成果:
- 在PI-CAI数据集上,自主监督的CSwin UNet在PI-CAI数据集上实现了AUC0.888和AP0.545.
- 该模型的性能明显优于其他可比方法,包括nnFormer,Swin UNETR,DynUNet,Attention UNet和UNet.
- 证明了强大的概括能力,在外部前列腺158数据集上实现了0.79的AUC和0.45的AP.
结论:
- 通过SSL预训练增强的CSwin UNet提供了一个强大的解决方案,用于端到端的csPCa检测.
- 拟议的框架提高了网络的通用性,并优于多机构数据集的现有方法.
- 使用自动加权损失 (AWL) 来统一借口任务,增强表示学习.
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