基于变压器的深度学习模型与形状指导,用于预测乳腺癌在乳房扫描图像中的图像
Kengo Takahashi1, Yuwen Zeng2, Zhang Zhang3
1Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8575, Japan.
Journal of imaging informatics in medicine
|December 19, 2025
概括
这项研究表明,指导视觉变压器 (ViT) 模型在特定层的乳腺口罩上改善了乳腺癌的检测. 蒙面ViT模型实现了比传统方法更高的准确性,帮助诊断.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 乳腺癌研究越来越多地使用视觉变压器 (ViT) 模型.
- 在ViT中,注意力指导侧重于解剖结构和瘤异质性.
- 对于注意力引导的最佳变压器编码器层阶段仍然不清楚.
研究的目的:
- 评估ViT模型中用于乳腺癌检测的形状指导策略.
- 为了确定注意力引导的最佳编码器层组合.
- 为了将拟议的蒙面变压器模型与传统方法进行比较.
主要方法:
- 应用胸罩对ViT模型的注意力机制,以强调空间依赖.
- 与ResNet50,ViT和SwinT V2进行比较的蒙面ViT模型.
- 利用了来自中国乳腺癌数据库的2,436张乳腺癌图像.
- 雇员三重交叉验证,培训分为70%和验证分为30%.
主要成果:
- 在浅层和深层阶段应用指导的蒙面ViT模型实现了最高的接收机操作特征曲线 (AUROC) 下的区域.
- 最好的蒙面ViT模型达到0.885的AUROC,表现优于常规模型.
- 获得了0.876的灵敏度和0.802.80的特异性.
结论:
- 形状指导策略,特别是当应用于特定的变压器编码器层时,可以增强ViT模型中的表示学习.
- 蒙面ViT模型显示出作为乳腺癌诊断决策支持工具的巨大潜力.
- 在ViT中优化注意力指导可以提高医疗成像任务的诊断准确性.
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