Deep Learning for Classification of Colorectal Polyps on Whole-slide Images.
Deep Learning for Classification of Colorectal Polyps on Whole-slide Images.
复制标题
DOI：
10.4103/jpi.jpi_34_17
复制
发表时间：
2017
影响因子：
--
通讯作者：
Hassanpour S
中科院分区：
文献类型：
--
作者：
Korbar B;Olofson AM;Miraflor AP;Nicka CM;Suriawinata MA;Torresani L;Suriawinata AA;Hassanpour S
结直肠息肉的组织学特征对于确定结直肠癌的风险和患者未来的监测率至关重要。然而，这种表征是一项具有挑战性的任务，并遭受显着的观察者间和观察者内的变化。我们建立了一种自动图像分析方法，可以准确地对全切片图像上不同类型的结直肠息肉进行分类，以帮助病理学家进行这种表征和诊断。我们的方法基于深度学习技术，该技术依赖于数据表示的多个抽象层次，并在各种图像分析任务中显示了最先进的结果。我们的方法涵盖了五种常见的息肉类型（即，增生的、无柄锯齿状的、传统锯齿状的、管状的和管状绒毛状的/绒毛状的），其被包括在美国多社会工作组的结直肠癌风险评估和监测指南中。我们通过利用2074个作物图像的数据集开发了多种深度学习方法，这些图像由多个领域的专家病理学家作为参考标准进行注释。我们在239张完整幻灯片图像的独立测试集上评估了我们的方法，并测量了准确度，精确度，召回率和F1分数及其95%置信区间的标准机器学习评估指标。我们的评估表明，我们的方法与残留网络架构实现了最佳性能的分类结直肠息肉的全载玻片图像（总体准确率：93.0%，95%置信区间：89.0%-95.9%）。我们的方法可以减轻病理学家的认知负担，提高他们在结直肠息肉组织病理学特征以及随后的风险评估和随访建议中的疗效。
Histopathological characterization of colorectal polyps is critical for determining the risk of colorectal cancer and future rates of surveillance for patients. However, this characterization is a challenging task and suffers from significant inter- and intra-observer variability. We built an automatic image analysis method that can accurately classify different types of colorectal polyps on whole-slide images to help pathologists with this characterization and diagnosis. Our method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks. Our method covers five common types of polyps (i.e., hyperplastic, sessile serrated, traditional serrated, tubular, and tubulovillous/villous) that are included in the US Multisociety Task Force guidelines for colorectal cancer risk assessment and surveillance. We developed multiple deep-learning approaches by leveraging a dataset of 2074 crop images, which were annotated by multiple domain expert pathologists as reference standards. We evaluated our method on an independent test set of 239 whole-slide images and measured standard machine-learning evaluation metrics of accuracy, precision, recall, and F1 score and their 95% confidence intervals. Our evaluation shows that our method with residual network architecture achieves the best performance for classification of colorectal polyps on whole-slide images (overall accuracy: 93.0%, 95% confidence interval: 89.0%–95.9%). Our method can reduce the cognitive burden on pathologists and improve their efficacy in histopathological characterization of colorectal polyps and in subsequent risk assessment and follow-up recommendations.