可解释性
计算机科学
卷积神经网络
宫颈癌
深度学习
模式识别(心理学)
人工智能
可视化
机器学习
学习迁移
医学
癌症
内科学
作者
Ying Chen,Xiaomin Qin,Jingyu Xiong,Shugong Xu,Jun Shi,Huabing Lv,Lin Li,Hui Xing,Qi Zhang
出处
期刊:Journal of Medical Imaging and Health Informatics
[American Scientific Publishers]
日期:2020-02-01
卷期号:10 (2): 391-400
被引量:4
标识
DOI:10.1166/jmihi.2020.2967
摘要
This study aimed to propose a deep transfer learning framework for histopathological image analysis by using convolutional neural networks (CNNs) with visualization schemes, and to evaluate its usage for automated and interpretable diagnosis of cervical cancer. First, in order to examine the potential of the transfer learning for classifying cervix histopathological images, we pre-trained three state-of-the-art CNN architectures on large-size natural image datasets and then fine-tuned them on small-size histopathological datasets. Second, we investigated the impact of three learning strategies on classification accuracy. Third, we visualized both the multiple-layer convolutional kernels of CNNs and the regions of interest so as to increase the clinical interpretability of the networks. Our method was evaluated on a database of 4993 cervical histological images (2503 benign and 2490 malignant). The experimental results demonstrated that our method achieved 95.88% sensitivity, 98.93% specificity, 97.42% accuracy, 94.81% Youden's index and 99.71% area under the receiver operating characteristic curve. Our method can reduce the cognitive burden on pathologists for cervical disease classification and improve their diagnostic efficiency and accuracy. It may be potentially used in clinical routine for histopathological diagnosis of cervical cancer.
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