计算机科学 ›› 2017, Vol. 44 ›› Issue (Z11): 1-7.doi: 10.11896/j.issn.1002-137X.2017.11A.001

• 综述研究 •    下一篇

基于深度学习的医学影像诊断综述

张巧丽,赵地,迟学斌   

  1. 中国科学院计算机网络信息中心 北京100190,中国科学院计算机网络信息中心 北京100190,中国科学院计算机网络信息中心 北京100190
  • 出版日期:2018-12-01 发布日期:2018-12-01

Review for Deep Learning Based on Medical Imaging Diagnosis

ZHANG Qiao-li, ZHAO Di and CHI Xue-bin   

  • Online:2018-12-01 Published:2018-12-01

摘要: 目前各类医学影像数据积累迅速,给利用传统影像分析方法实现疾病诊断的医生带来了巨大挑战;计算机视觉领域的深度学习方法日渐成熟,为实现医学影像的自动分析及辅助医生实现疾病的高精度智能诊断提供了新的契机。文中综述了深度学习方法在医学影像领域的最新研究进展。首先,介绍了深度学习方法以及该类方法在医学影像领域的应用情况;然后,从应用深度学习主要研究的几大病症来分析具体的研究进展;最后,总结研究动向,预测研究趋势,并提出深度学习在医学影像研究中可能存在的问题以及建议。

关键词: 深度学习,疾病诊断,医学影像

Abstract: At present,the various modalities of medical image data accumulate rapidly,bringing great challenges to doctors who diagnose disease through traditional medical image analysis methods.Deep learning method has gained great success and become more and more popular in the computer vision field.All that case provides new chances for automaticmedical image analysis and makes high precisely computer-aided disease diagnosis possible.In this paper,we reviewed state-of-the-art research progress of deep learning in the medical image field.Firstly,the method of deep learning and its application in the field of medical imaging are introduced.Then attention is focused on specific research progress of deep learning method in several typical and popular disease.Finally,the tendency of this research field is summarized,and then the existing problems and recommendations are put forward.

Key words: Deep learning,Disease diagnosis,Medical imaging

[1] LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,1(7553):436-444.
[2] DENG L,YU D.Deep Learning[J].Signal Processing,2014,7:3-4.
[3] SONG H A,LEE S Y.Hierarchical Representation Using NMF[C]∥International Conference on Neural Information Proces-sing.Springer Berlin Heidelberg,2013:466-473.
[4] HUBEL D H,WIESEL T N.Receptive fields,binocular interaction and functional architecture in the cat’s visual cortex[J].The Journal of physiology,1962,0(1):106-154.
[5] FUKUSHIMA K.A hierarchical neural network model for associative memory[J].Biological cybernetics,1984,0(2):105-113.
[6] BENGIO Y,LAMBLIN P,POPOVICI D,et al.Greedy layer-wise training of deep networks[J].Advances in neural information processing systems,2007,9:153.
[7] HINTON G E,SEJNOWSKI T J.Learning and releaming in Boltzmann machines[J].Parallel distributed processing:Explorations in the Microstructure of Cognition,1986,1:282-317.
[8] HINTON G E,OSINDERO S,TEH Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,8(7):1527-1554.
[9] ALZHEIMER’S A.Alzheimer’s disease facts and figures[J].Alzheimer’s & Dementia,2014,0(2):e47-e92.
[10] LIU S,CAI W,et al.Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease[J].IEEE Transactions on Biomedical Engineering,2015,2(4):1132-1140.
[11] SUK H I,SHEN D.Deep learning-based feature representation for AD/MCI classification[C]∥MICCAI.2013:583-590.
[12] PAYAN A,MONTANA G.Predicting Alzheimer’s disease:aneuroimaging study with 3D convolutional neural networks.http://arxiv.org/pdf/1502.02506.pdf.
[13] HOSSEINI-ASL E,GIMEL’FARB G,EL-BAZ A.Alzheimer’s Disease Diagnostics by a Deeply Supervised Adaptable 3D Con-volutional Network.https://arxiv.org/pdf/1607.00556.pdf.
[14] SARRAF S,TOFIGHI G.DeepAD:Alzheimer′ s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI.https://www.biorxiv.org/content/biorxiv/early/2016/08/21/070441.full.pdf.
[15] SARRAF S,TOFIGHI G.Deep Learning-based Pipeline to Re-cognize Alzheimer’s Disease using fMRI Data.https://ar-xiv.org/pdf/1610.07231.pdf.
[16] 吕鸿蒙,赵地,迟学斌.基于增强AlexNet的深度学习的阿尔茨海默病的早期诊断[J].计算机科学,2017,44(s1):50-60.
[17] AL-FATLAWI A H,JABARDI M H,LING S H.Efficient diagnosis system for Parkinson’s disease using deep belief network[C]∥IEEE Evolutionary Computation.2016:1324-1330.
[18] SHAMIR R R,DOLBER T,NOECKER A M,et al.Machinelearning approach to optimizing combined stimulation and medication therapies for Parkinson’s disease[J].Brain Stimulation,2015,8(6):1025-1032.
[19] SUN W,ZHENG B,QIAN W.Computer aided lung cancer diagnosis with deep learning algorithms[C]∥SPIE Medical Imaging.2016.
[20] PAN H,XU Z,HANG J.An effective approach for robust lung cancer detection[C]∥International Workshop on Patch-based Techniques in Medical Imaging.2015:87-94.
[21] SPANHOL F A,OLIVERIRA L S,P ETITJEAN C,et al.Breast Cancer Histopathological Image Classification using Convolutional Neural Networks[C]∥International Joint Conference on Neural Networks.2016:2560-2567.
[22] WANG D,KHOSLA A,GARGEYA R,et al.Deep learning for identifying metastatic breast cancer.https://arxiv.org/pdf/1606.05718.pdf.
[23] S,AN D C,GIUSTI A,GAMBARDELLA L M,et al.Mitosis detection in breast cancer histology images with deep neural networks[C]∥International Conference on Medical Image Computing and Computer-assisted Intervention.2013:411-418.
[24] TIRUMALA S,NARAYANAN A.Attribute Selection and Clas-sification of Prostate Cancer Gene Expression Data Using Artificial Neural Networks[C]∥Pacific-Asia Conference on Know-ledge Discovery and Data Mining.2016:26-34.
[25] AZIZI S,IMANI F,ZHUANG B,et al.Ultrasound-Based Detection of Prostate Cancer Using Automatic Feature Selection with Deep Belief Networks[C]∥International Conference on Medical Image Computing and Computer-Assisted Intervention.2015:70-77.
[26] XU T,ZHANG H,HUANG X,et al.Multimodal Deep Learning for Cervical Dysplasia Diagnosis[C]∥International Conference on Medical Image Computing and Computer-Assisted Intervention.2016:115-123.
[27] DEVI M A,RAVI S,VAISHNAVI J,et al.Classification of Cervical Cancer Using Artificial Neural Networks[J].Procedia Computer Science,2016,9:465-472.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!