相关实验视频
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Methods to Test Visual Attention Online
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使用流和注意力进行千兆像素端到端训练.
Stephan Dooper1, Hans Pinckaers1, Witali Aswolinskiy1
1Computational Pathology Group, Department of Pathology, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands.
Medical image analysis
|July 12, 2023
概括
StreamingCLAM允许使用幻灯片级标签对千兆像素显微镜图像进行端到端的训练. 这种弱监督学习方法在检测转移性乳腺癌和MYC基因转位方面取得了很高的准确性.
科学领域:
- 计算病理学计算病理学
- 数字病理学数字病理学
- 机器学习在医学中的应用
背景情况:
- 硬件限制使得卷积神经网络无法在千兆像素图像上进行直接训练.
- 现有的弱监督学习方法经常使用多阶段或补丁智能的策略,冒着低于最佳特征提取的风险.
研究的目的:
- 为千兆像素显微镜图像提出一个端到端的训练方法,只使用幻灯片级标签.
- 克服硬件限制,在大型医学图像上训练深度学习模型.
主要方法:
- 开发了 StreamingCLAM,一个带有注意力关闭分类头的 ResNet-34 编码器,使用卷积层的流式实现.
- 启用了对具有幻灯片级标签的4千兆像素图像的端到端训练.
主要成果:
- 实现了转移性乳腺癌检测ROC曲线下的0.9757的平均面积 (CAMELYON16).
- 成功检测到MYC基因转位在扩散大B细胞淋巴瘤中,平均AUC为0.8259.
- 通过注意力机制证明了可解释性.
结论:
- StreamingCLAM 促进了千兆像素图像的端到端训练,实现了与完全监督方法相提并论的性能.
- 注意力机制为模型预测提供了洞察力,提高了可解释性.
- 这种方法为大规模的计算病理学任务提供了可行的解决方案.
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