Weakly-supervised learning for lung carcinoma classification using deep learning
Weakly-supervised learning for lung carcinoma classification using deep learning
复制标题
使用深度学习进行肺癌分类的弱监督学习
DOI：
10.1038/s41598-020-66333-x
复制
发表时间：
2020-06-09
影响因子：
4.6
通讯作者：
Tsuneki, Masayuki
中科院分区：
文献类型：
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作者：
Kanavati, Fahdi;Toyokawa, Gouji;Tsuneki, Masayuki
肺癌是世界许多国家癌症相关死亡的主要原因之一，其组织病理学诊断对于决定最佳治疗策略至关重要。近年来，人工智能（AI）深度学习模型已被广泛应用于各个医学领域，特别是图像和病理诊断；然而，在大规模测试集上得到验证的用于肺部病变病理诊断的人工智能模型尚未出现。我们使用迁移学习和弱监督学习训练了基于 EfficientNet-B3 架构的卷积神经网络 (CNN)，以使用 3,554 个 WSI 的训练数据集来预测整个幻灯片图像 (WSI) 中的癌症。我们在四个独立测试集（ROC AUC 分别为 0.975、0.974、0.988 和 0.981）上获得了区分肺癌和非肿瘤的非常有希望的结果，具有高受试者工作曲线 (ROC) 曲线下面积 (AUC)。像我们这样的算法的开发和验证是开发软件套件的重要初始步骤，这些软件套件可以在常规病理实践中采用，并可能有助于减轻病理学家的负担。
Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, and its histopathological diagnosis is crucial for deciding on optimum treatment strategies. Recently, Artificial Intelligence (AI) deep learning models have been widely shown to be useful in various medical fields, particularly image and pathological diagnoses; however, AI models for the pathological diagnosis of pulmonary lesions that have been validated on large-scale test sets are yet to be seen. We trained a Convolution Neural Network (CNN) based on the EfficientNet-B3 architecture, using transfer learning and weakly-supervised learning, to predict carcinoma in Whole Slide Images (WSIs) using a training dataset of 3,554 WSIs. We obtained highly promising results for differentiating between lung carcinoma and non-neoplastic with high Receiver Operator Curve (ROC) area under the curves (AUCs) on four independent test sets (ROC AUCs of 0.975, 0.974, 0.988, and 0.981, respectively). Development and validation of algorithms such as ours are important initial steps in the development of software suites that could be adopted in routine pathological practices and potentially help reduce the burden on pathologists.