3D视觉转换器堆叠组合用于评估T2w图像的前列腺癌攻击性.
Eva Pachetti1,2, Sara Colantonio1
1"Alessandro Faedo" Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), 56127 Pisa, Italy.
Bioengineering (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了一种新的3D视觉转换器组合,用于从MRI扫描中检测前列腺癌的攻击性,旨在减少对活检的需求. 最好的模型实现了最先进的性能,提高了诊断准确度.
科学领域:
- 计算机视觉 计算机视觉
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 视觉转换器是先进的AI模型,通常用于2D图像分析.
- 目前评估前列腺癌攻击性的方法通常需要侵入性活检.
- 需要使用非侵入性方法来准确诊断癌症的严重程度.
研究的目的:
- 开发和评估一个从头开始训练的3D视觉转换器堆叠组合,用于评估前列腺癌的攻击性.
- 利用T2加权的MRI图像进行非侵入性癌症诊断.
- 提高诊断准确度,减少对活检的依赖.
主要方法:
- 在T2加权的MRI数据上训练了18个单独的3D视觉转换器 (ViT).
- 将ViT组合成两种和三种模型的堆叠组合.
- 采用五倍交叉验证来评估准确性,预测信心和校准.
- 优化了基础ViT,并将组合性能与基础模型进行了比较.
主要成果:
- 最好的3D视觉变压器堆叠组合实现了最先进的接收操作曲线下的面积 (0.89).
- 与基准模型相比,整体显著提高了精度回忆曲线下的面积,提高了22% (p < 0.001).
- 整体表现出较低的信心分类阳性病例,尽管高准确度.
结论:
- 一个3D视觉转换器堆叠组合可以有效地评估前列腺癌的攻击性从T2加权的MRI.
- 这种方法为传统的活检方法提供了一个有希望的非侵入性替代方案.
- 需要进一步的研究来提高组合模型的临床应用的信心指标.
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