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在MRI上进行前列腺癌检测的少数镜头学习:与放射科医生的性能进行比较分析

Yosuke Yamagishi^1,2, Yasutaka Baba³, Jun Suzuki³

¹Department of Diagnostic Radiology, Saitama Medical University International Medical Center, Saitama Medical University International Medical Center, Hidaka, Japan. yamagishi-yosuke0115@g.ecc.u-tokyo.ac.jp.

Journal of imaging informatics in medicine

|June 25, 2025

概括

少数拍摄的深度学习模型显示,使用最小的MRI数据来检测前列腺癌具有前景. 这些模型实现了与放射科医生可比的性能,解决了临床环境中的数据限制和领域转移问题.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

对于前列腺癌检测而言,深度学习需要大量的数据集,而这些数据集往往因跨机构的领域转移问题而受到阻碍.
数据的有限可用性限制了当前深度学习模型的临床适用性.

研究的目的:

在多参数核磁共振 (MRI) 上开发一个用于前列腺癌检测的几次学习深度学习模型.
为了评估这个模型的诊断性能,与经验丰富的放射科医生进行对比.
解决人工智能驱动的癌症检测中的数据稀缺和领域转移挑战.

主要方法:

一个2D变压器模型被训练在T2加权,扩散加权和99个活检确认的前列腺癌病例的明显扩散系数 (ADC) 地图图像上.
该模型使用了几次射击的学习方法,训练数据最小 (20个案例).
使用马修斯相关系数 (MCC) 和F1得分来评估性能,与两个放射科医生进行比较,并在前列腺158数据集上进行外部验证.

主要成果:

几次拍摄的模型实现了0.297的MCC和0.707的F1得分,相当于放射科医生1 (MCC:0.276,F1:0.741).
放射科医生2的表现优于模型 (MCC:0.504,F1:0.871).
ImageNet预训练显著改善了模型性能,研究水平ROC-AUC从0.464增加到0.636和PR-AUC从0.637增加到0.773.

结论:

少数拍摄的深度学习模型,特别是预训练过的变压器架构,可以实现前列腺癌检测的临床相关性能.
这种方法提供了一种可行的解决方案,以克服数据限制和多机构环境中的域移动问题.
这些发现支持人工智能的潜力,以提高前列腺癌成像诊断能力.

关键词:

在美国,CNN是CNN.有几次射击学习学习.影像网预培训影像网预培训这就是为什么MRI是MRI.马姆巴·马姆巴是什么意思前列腺癌是什么意思前列腺癌是什么意思变压器变压器变压器

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