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  • CN 31-1691/TQ

一种基于师生间注意力的AD诊断模型

李宜儒 罗健旭

李宜儒, 罗健旭. 一种基于师生间注意力的AD诊断模型[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20220303001
引用本文: 李宜儒, 罗健旭. 一种基于师生间注意力的AD诊断模型[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20220303001
LI Yiru, LUO Jianxu. Knowledge Distillation of Teacher-Student Attention Alzheimer's Disease[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20220303001
Citation: LI Yiru, LUO Jianxu. Knowledge Distillation of Teacher-Student Attention Alzheimer's Disease[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20220303001

一种基于师生间注意力的AD诊断模型

doi: 10.14135/j.cnki.1006-3080.20220303001
基金项目: 上海市科技创新行动计划项目(19511121203)
详细信息
    作者简介:

    李宜儒(1996—),男,浙江人,硕士生,主要研究方向为医学影像小样本。E-mail: 837806012@qq.com

    通讯作者:

    罗健旭,E-mail: jxluo@ecust.edu.cn

  • 中图分类号: R445.2

Knowledge Distillation of Teacher-Student Attention Alzheimer's Disease

  • 摘要: 通过核磁影像(MRI)分析实现阿尔兹海默症(AD)的早期诊断具有重要意义,但基于深度学习的MRI影像分析通常面对训练样本量小及3D数据计算成本高的挑战。本文提出了一种CNN和LSTM级联的AD诊断模型,先用CNN提取图像的深层特征,再用LSTM对序列特征进行分类,这样将3D的MRI图像视为2D图像的序列,考虑了切片序列间的关联信息。另外,为了提高模型在小样本数据上的性能表现,应用知识蒸馏算法以得到压缩的轻量级模型,并引入师生间的注意力机制提高模型分类的准确率。实验表明,该诊断模型在ADNI数据集上取得了良好的性能。

     

  • 图  1  DenseNet-LSTM的结构图

    Figure  1.  The structure diagram of DenseNet-LSTM

    图  2  知识蒸馏框架

    Figure  2.  The structure of knowledge distillation

    图  3  师生间注意力流程图

    Figure  3.  Flow chart of attention between student and teacher

    图  4  AT-KD-den169-den121模型的混淆矩阵

    Figure  4.  Confusion matrix of the AT-KD-den169-den121 model

    表  1  AD的三视图的各个准确率

    Table  1.   Accuracy of each of the three views of AD

    ViewSagittal view/%Coronal view/%Axial view/%
    Accuracy82.1283.8381.70
    下载: 导出CSV

    表  2  各模型性能比较

    Table  2.   Comparison of the performance of each model

    ModelTeacherDenseNet169Acc = 82.12%
    Accuracy/%Precision/%Recall/%F1-Scores/%
    Student-Den12182.12882.36382.06682.035
    KD-den169-den12185.95785.96886.04285.773
    AT-KD-den169-den12186.38285.71086.40385.800
    下载: 导出CSV
  • [1] PATTERSON C. The state of the art of dementia research: New frontiers [R]. London: World Alzheimer Report, 2018.
    [2] FAROOQ A, ANWAR S, AWAIS M, et al. A deep CNN based multi-class classification of Alzheimer's disease using MRI[C]// 2017 IEEE International Conference on Imaging systems and techniques (IST), Beijing, IEEE, 2017: 1-6.
    [3] 于鲁, 刘卫芳. 海马与胼胝体3D纹理分析在阿尔兹海默症诊断中的比较[J]. 中国医疗设备, 2016, 31(10): 29-32. doi: 10.3969/j.issn.1674-1633.2016.10.009
    [4] ZHU Q, ZHANG D, LI H, et al. Incomplete multi-modal brain image fusion for epilepsy classification[J]. Information Sciences, 2022, 582: 316-333. doi: 10.1016/j.ins.2021.09.035
    [5] XU M, ZHANG T, ZHANG D. Medrdf: a robust and retrain-less diagnostic framework for medical pretrained models against adversarial attack[J]. IEEE Transactions on Medical Imaging, 2022: 1-1. doi: 10.1109/TMI.2022.3156268
    [6] SUBRAMONIAM M. Deep learning based prediction of Alzheimer's disease from magnetic resonance images [EB/OL]. arxiv. org, (2021-01-13)[2022-05-21].https://arxiv.org/abs/2101.04961.
    [7] NAWAZ A, ANWAR S M, LIAQAT R, et al. Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI Data[C]// 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, IEEE, 2020: 1-6.
    [8] XING X, LIANG G, BLANTON H, et al. Dynamic Image for 3D MRI Image Alzheimer’s Disease Classification[C]// European Conference on Computer Vision, Springer, 2020: 355-364.
    [9] KOROLEV S, SAFIULLIN A, BELYAEV M, et al. Residual and plain convolutional neural networks for 3D brain MRI classification[C]// 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), IEEE, 2017: 835-838.
    [10] HINTON G, VINYALS O, DEAN J. Distilling the Knowledge in a Neural Network[J]. Computer Science, 2015, 14(7): 38-39.
    [11] YANG Y, XUTAO G, YE C, et al. Regularizing Brain Age Prediction via Gated Knowledge Distillation[C]// Proceedings of Medical Imaging with Deep Learning (MIDL), 2022.
    [12] GUAN H, WANG C, TAO D. MRI-based Alzheimer's disease prediction via distilling the knowledge in multi-modal data[J]. NeuroImage, 2021, 244: 118586. doi: 10.1016/j.neuroimage.2021.118586
    [13] HU J, SHEN L, SUN G. Squeeze-and-Excitation Networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 42(8): 2011-2023.
    [14] WOO S, PARK J, LEE J Y, et al. Cbam: Convolutional block attention module[C]// Proceedings of the European conference on computer vision (ECCV), 2018: 3-19.
    [15] PASSBAN P, WU Y, REZAGHOLIZADEH M, et al. ALP-KD: Attention-based layer projection for knowledge distillation[C]// Proceedings of the AAAI Conference on Artificial Intelligence, 2021: 13657-13665.
    [16] JI M, HEO B, PARK S. Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching[C]// Proceedings of the AAAI Conference on Artificial Intelligence, 2021: 7945-7952.
    [17] YU Y, SI X, HU C, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural computation, 2019, 31(7): 1235-1270. doi: 10.1162/neco_a_01199
    [18] 肖顺, 储呈晨, 王源冰, 等. 压缩感知技术在磁共振成像技术中的应用进展分析[J]. 中国医疗设备, 2021, 36(11): 4-9.
    [19] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]// Proceedings of the IEEE conference on computer vision and pattern recognition, 2017: 4700-4708.
    [20] YOSINSKI J, CLUNE J, BENGIO Y, et al. How transferable are features in deep neural networks?[C]// Neural Information Processing Systems (NIPS 2014), 2014.
    [21] GUO H, ZHENG K, FAN X, et al. Visual attention consistency under image transforms for multi-label image classification[C]// Conference on Computer Vision and Pattern Recognition, IEEE, 2019: 729-739.
    [22] BELLO I, ZOPH B, VASWANI A, et al. Attention augmented convolutional networks[C]// Proceedings of the IEEE/CVF international conference on computer vision, 2019: 3286-3295.
    [23] SHENG J, XIN Y, ZHANG Q, et al. Predictive Classification of Alzheimer’s Disease Using Brain Imaging and Genetic Data[J]. Scientific Reports, 2022, 12(1): 1-9. doi: 10.1038/s41598-021-99269-x
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出版历程
  • 收稿日期:  2022-03-03
  • 网络出版日期:  2022-05-26

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