Potential of Attention Mechanism for Classification of Optical Coherence Tomography Images

Deep neural network (DNN) can extract high-dimensional feature of images for computer vision tasks including Optical Coherence Tomography (OCT) images classification. However, OCT images are usually processed by DNN just like natural images, thus the performance of DNN is not satisfactory. We present an end-to-end DNN targeting OCT images classification. Considering the characteristic of OCT images, we introduce attention mechanism into classifier to extract more specific feature of OCT images. Our network demonstrates its capacity to enhance the features that represent the disease region. Our method achieves the state-of-the-art performance with average accuracy of 99.5% and F1-score of 0.995 on the OCT images dataset.

[1]  C. V. Jawahar,et al.  The truth about cats and dogs , 2011, 2011 International Conference on Computer Vision.

[2]  Yi Yang,et al.  Attention to Scale: Scale-Aware Semantic Image Segmentation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[4]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[6]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[8]  Pietro Perona,et al.  Bird Species Categorization Using Pose Normalized Deep Convolutional Nets , 2014, ArXiv.

[9]  Yongdong Zhang,et al.  Convolutional Attention Networks for Scene Text Recognition , 2019, ACM Trans. Multim. Comput. Commun. Appl..

[10]  Samy Bengio,et al.  Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Xiaogang Wang,et al.  Residual Attention Network for Image Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Jonathan Krause,et al.  Fine-grained recognition without part annotations , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Yuan Zhang,et al.  Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function , 2018, Neurocomputing.

[14]  Yongdong Zhang,et al.  Automated pulmonary nodule detection in CT images using deep convolutional neural networks , 2019, Pattern Recognit..

[15]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Daniel S. Kermany,et al.  Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.

[18]  John S. Zelek,et al.  Classification of optical coherence tomography images for diagnosing different ocular diseases , 2018, BiOS.

[19]  Tao Mei,et al.  Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).