Attention-based multiple instance learning with self-supervision to predict microsatellite instability in colorectal cancer from histology whole-slide images.
Attention-based multiple instance learning with self-supervision to predict microsatellite instability in colorectal cancer from histology whole-slide images.
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DOI：
10.1109/embc48229.2022.9871553
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发表时间：
2022-07-01
期刊：
影响因子：
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通讯作者：
Kim, Dokyoon
中科院分区：
文献类型：
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作者：
Leiby, Jacob S;Hao, Jie;Kim, Dokyoon
微卫星不稳定性（MSI）是结直肠癌的临床重要特征。 MSI 的标准诊断是通过基因分析进行的，但这些测试并不总是包含在常规护理中。组织病理学全切片图像 (WSI) 是结直肠癌诊断的金标准，并且是常规收集的。本研究开发了一个模型来直接根据 WSI 预测 MSI。利用弱监督和自监督深度学习技术，所提出的模型显示出比传统深度学习模型更高的性能。此外，所提出的框架允许对模型决策进行视觉解释。这些结果在内部和外部测试数据集中得到验证。
Microsatellite instability (MSI) is a clinically important characteristic of colorectal cancer. Standard diagnosis of MSI is performed via genetic analyses, however these tests are not always included in routine care. Histopathology whole-slide images (WSIs) are the gold-standard for colorectal cancer diagnosis and are routinely collected. This study develops a model to predict MSI directly from WSIs. Making use of both weakly- and self-supervised deep learning techniques, the proposed model shows improved performance over conventional deep learning models. Additionally, the proposed framework allows for visual interpretation of model decisions. These results are validated in internal and external testing datasets.