[1]付常洋,王瑜,肖洪兵,等.基于深度学习与结构磁共振成像的抑郁症辅助诊断[J].智能系统学报,2021,16(3):544-551.[doi:10.11992/tis.201912006]
FU Changyang,WANG Yu,XIAO Hongbing,et al.Assisted diagnosis of major depression disorder using deep learning and structural magnetic resonance imaging[J].CAAI Transactions on Intelligent Systems,2021,16(3):544-551.[doi:10.11992/tis.201912006]
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FU Changyang,WANG Yu,XIAO Hongbing,et al.Assisted diagnosis of major depression disorder using deep learning and structural magnetic resonance imaging[J].CAAI Transactions on Intelligent Systems,2021,16(3):544-551.[doi:10.11992/tis.201912006]
基于深度学习与结构磁共振成像的抑郁症辅助诊断
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第3期
页码:
544-551
栏目:
学术论文—人工智能基础
出版日期:
2021-05-05
- Title:
- Assisted diagnosis of major depression disorder using deep learning and structural magnetic resonance imaging
- Keywords:
- depression; diagnosis; computer vision; deep learning; optimization; transfer learning; structural magnetic resonance image; classification
- 分类号:
- TP181
- 摘要:
- 抑郁症是致残率和发病率最高的疾病之一，全球约有3亿人正遭受着抑郁症的困扰。然而，目前并没有有效的生物特征和临床方法能够帮助医生对抑郁症进行准确的诊断。针对此任务，本文将计算机视觉领域的前沿深度学习模型进行优化与适配，应用于抑郁症的辅助诊断，并在此基础上引入迁移学习，取得了很好的效果。实验结果表明，同前沿算法模型相比，本文提出的方法能够有效提高抑郁症与健康对照者的结构磁共振成像分类准确率和召回率，充分验证了提出方法的有效性和优越性。
- Abstract:
- Depression is one of the diseases with the highest disability and morbidity. About 300 million people around the world are suffering from depression. However, there exist no effective biological characteristics and clinical methods to help doctors diagnose depression accurately. In this study, the state-of-the-art deep learning model in the field of computer vision is optimized and adapted to diagnose depression. On this basis, transfer learning is introduced, achieving excellent results. Experimental results reveal that compared with the frontier algorithm model, the proposed method can effectively improve the classification accuracy and recall of the structural magnetic resonance image of control subjects who are healthy and those who are depressed, which fully verifies the effectiveness and superiority of the proposed method.
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- 相似文献/References:
-
[1]廖智舟,李川,周军,等.抑郁症静息态EEG前后部脑电活动[J].智能系统学报,2014,9(2):168.[doi:10.3969/j.issn.1673-4785.201307017]
LIAO Zhizhou,LI Chuan,ZHOU Jun,et al.Resting EEG based disorders in the anterior and posterior brain in depression[J].CAAI Transactions on Intelligent Systems,2014,9():168.[doi:10.3969/j.issn.1673-4785.201307017]
备注/Memo
收稿日期:2019-12-07。
基金项目:国家自然科学基金面上项目(61671028)；国家重大科技研发子课题(ZLJC6 03-5-1)
作者简介:付常洋，硕士研究生，主要研究方向为图像处理与机器学习;王瑜，副教授，博士，中国自动化学会、中国电子学会和中国人工智能学会高级会员，生物信息学与人工生命专委会委员，IEEE和计算机学会会员，CCFYOCSEF委员，主要研究方向为图像处理与模式识别。主持国家自然科学基金面上项目2项、北京市自然科学基金面上项目1项。出版学术专著2部，发表学术论文30余篇;肖洪兵，副教授，博士，主要研究方向为传感器与高动态测试技术、嵌入式系统应用。在研以及完成的科研项目10余项，其中省级以上项目3项。获得北京市科技进步三等奖1项。取得软件著作权3项，实用新型专利3项。出版专著1部，主编教材3部，发表学术论文20余篇
通讯作者:王瑜.E-mail:wangyu@btbu.edu.cn
更新日期/Last Update:
2021-06-25