Deep Feature Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network

At present, computed tomography (CT) is widely used to assist disease diagnosis. Especially, computer aided diagnosis (CAD) based on artificial intelligence (AI) recently exhibits its importance in intelligent healthcare. However, it is a great challenge to establish an adequate labeled dataset for CT analysis assistance, due to the privacy and security issues. Therefore, this paper proposes a convolutional autoencoder deep learning framework to support unsupervised image features learning for lung nodule through unlabeled data, which only needs a small amount of labeled data for efficient feature learning. Through comprehensive experiments, it shows that the proposed scheme is superior to other approaches, which effectively solves the intrinsic labor-intensive problem during artificial image labeling. Moreover, it verifies that the proposed convolutional autoencoder approach can be extended for similarity measurement of lung nodules images. Especially, the features extracted through unsupervised learning are also applicable in other related scenarios.

[1]  Wei Shen,et al.  Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification , 2017, Pattern Recognit..

[2]  Nico Karssemeijer,et al.  Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring , 2016, IEEE Transactions on Medical Imaging.

[3]  Berkman Sahiner,et al.  Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. , 2009, Medical physics.

[4]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[6]  Thomas Hofmann,et al.  Greedy Layer-Wise Training of Deep Networks , 2007 .

[7]  Min Chen,et al.  Big-Data Analytics for Cloud, IoT and Cognitive Computing , 2017 .

[8]  Jürgen Schmidhuber,et al.  Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction , 2011, ICANN.

[9]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[10]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[11]  Liang Zhou,et al.  On Data-Driven Delay Estimation for Media Cloud , 2016, IEEE Transactions on Multimedia.

[12]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[13]  Hong Zhao,et al.  A texture feature analysis for diagnosis of pulmonary nodules using LIDC-IDRI database , 2013, 2013 IEEE International Conference on Medical Imaging Physics and Engineering.

[14]  Alexander Wong,et al.  Lung Nodule Classification Using Deep Features in CT Images , 2015, 2015 12th Conference on Computer and Robot Vision.

[15]  Ronald M. Summers,et al.  Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[16]  Wei Shen,et al.  Multi-scale Convolutional Neural Networks for Lung Nodule Classification , 2015, IPMI.

[17]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Bram van Ginneken,et al.  Automatic cerebrospinal fluid segmentation in non-contrast CT images using a 3D convolutional network , 2017, Medical Imaging.

[19]  Jacques Wainer,et al.  Automatic breast density classification using a convolutional neural network architecture search procedure , 2015, Medical Imaging.

[20]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[21]  Takeo Kanade,et al.  Interactive Cell Segmentation Based on Active and Semi-Supervised Learning , 2016, IEEE Transactions on Medical Imaging.

[22]  Min Chen,et al.  Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring , 2016, Mobile Networks and Applications.

[23]  Maryellen L. Giger,et al.  Breast image feature learning with adaptive deconvolutional networks , 2012, Medical Imaging.

[24]  Marleen de Bruijne,et al.  Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines , 2016, IEEE Transactions on Medical Imaging.

[25]  Xiaogang Wang,et al.  Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.

[27]  Liang Zhou,et al.  QoE-Driven Delay Announcement for Cloud Mobile Media , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[28]  Min Chen,et al.  Emotion Communication System , 2017, IEEE Access.

[29]  Samuel H. Hawkins,et al.  Reproducibility and Prognosis of Quantitative Features Extracted from CT Images. , 2014, Translational oncology.

[30]  Wei Shen,et al.  Lung nodule malignancy prediction using multi-task convolutional neural network , 2017, Medical Imaging.

[31]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[32]  David Dagan Feng,et al.  Lung image patch classification with automatic feature learning , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[33]  Georg Langs,et al.  Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks , 2015, IPMI.

[34]  Min Chen,et al.  Wearable 2.0: Enabling Human-Cloud Integration in Next Generation Healthcare Systems , 2017, IEEE Communications Magazine.

[35]  Temesguen Messay,et al.  Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset , 2015, Medical Image Anal..

[36]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.