分层深度学习Ensemble通过ICD-O Topography自动分类乳腺癌病理报告
Hierarchical Deep Learning Ensemble to Automate the Classification of Breast Cancer Pathology Reports by ICD-O Topography
摘要：
与大多数全球癌症登记处一样，南非国家癌症登记处聘请专家编码员使用涵盖42种不同癌症类型的适当国际肿瘤疾病分类（ICD-O）代码标记病理报告。对于登记处每年从公共和私营部门机构收到的大量癌症病理报告，注释是广泛的。这一手工程序，加上其他挑战，导致南非在报告年度癌症统计数据方面严重滞后4年。我们提出了一种分层深度学习集成方法，该方法结合了最先进的卷积神经网络模型，用于自动标记2201份去识别的自由文本病理报告，并在8个类别中使用适当的ICD-0乳腺癌地形图代码。我们的研究结果显示，与最先进的CNN模型相比，F1微观评分的主要部位分类改善了14%以上，F1宏观评分改善了55%以上。我们证明，分层深度学习集成改进了最先进的模型，用于ICD-O拓扑分类相比，一个平面的多类模型预测ICD-O拓扑代码的病理报告。
英文摘要：
Like most global cancer registries, the National Cancer Registry in South Africa employs expert human coders to label pathology reports using appropriate International Classification of Disease for Oncology (ICD-O) codes spanning 42 different cancer types. The annotation is extensive for the large volume of cancer pathology reports the registry receives annually from public and private sector institutions. This manual process, coupled with other challenges results in a significant 4-year lag in reporting of annual cancer statistics in South Africa. We present a hierarchical deep learning ensemble method incorporating state of the art convolutional neural network models for the automatic labelling of 2201 de-identified, free text pathology reports, with appropriate ICD-O breast cancer topography codes across 8 classes. Our results show an improvement in primary site classification over the state of the art CNN model by greater than 14% for F1 micro and 55% for F1 macro scores. We demonstrate that the hierarchical deep learning ensemble improves on state-of-the-art models for ICD-O topography classification in comparison to a flat multiclass model for predicting ICD-O topography codes for pathology reports.
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