| 基于深度学习的甲状腺癌病理图像分级方法 |
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| 引用本文： | 曹莉凌1,蒋坷宏1,曹守启1,蒋伏松2. 基于深度学习的甲状腺癌病理图像分级方法[J]. 中国医学物理学杂志, 2023, 0(5): 580-588. DOI: DOI:10.3969/j.issn.1005-202X.2023.05.010 |
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| 作者姓名： | 曹莉凌1 蒋坷宏1 曹守启1 蒋伏松2 |
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| 作者单位： | 1.上海海洋大学工程学院， 上海 201306； 2.上海交通大学医学院附属第六人民医院内分泌代谢科， 上海 200233 |
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| 摘 要： | 针对日益增长的甲状腺癌早期诊断的需求，基于深度学习方法，在EfficientNet网络的基础上结合CA注意力机制，进行甲状腺癌病理图像自动分级方法研究。实验结果显示，CA-EfficientNet网络模型的精确率达到96.6%，证明了基于CA-EfficientNet网络的甲状腺癌病理图像自动分级算法的先进性，基于该算法实现的自动辅助诊断系统具有实际应用性，可有效降低病理医生工作负担，并降低因疲劳等主观因素造成的人工诊断误诊率。
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| 关 键 词： | 深度学习 甲状腺癌 卷积神经网络 全切片数字化图像 图像分级 |
Automatic grading of pathological images of thyroid cancer based on deep learning |
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| CAO Liling1,JIANG Kehong1,CAO Shouqi1,JIANG Fusong2. Automatic grading of pathological images of thyroid cancer based on deep learning[J]. Chinese Journal of Medical Physics, 2023, 0(5): 580-588. DOI: DOI:10.3969/j.issn.1005-202X.2023.05.010 |
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| Authors: | CAO Liling1 JIANG Kehong1 CAO Shouqi1 JIANG Fusong2 |
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| Affiliation: | 1. College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China 2. Department of Endocrinology and Metabolism, Shanghai Sixth Peoples Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200233, China |
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| Abstract: | Abstract: In response to the increasing demand for early diagnosis of thyroid cancer, a deep learning based method is proposed for the automatic grading of the pathological images of thyroid cancer through EfficientNet combined with CA-Net. The experimental results show that the accuracy of CA-EfficientNet model is up to 96.6%, which proves the algorithm superiority in the automatic grading of the pathological images of thyroid cancer. The automatic auxiliary diagnosis system implemented based on the proposed algorithm is applicable in practice for it can effectively reduce the workload of pathologists and reduce the rate of misdiagnosis caused by subjective factors such as fatigue. |
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| Keywords: | Keywords: deep learning thyroid cancer convolutional neural network whole slide image image grading |
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