Efficient Deep Learning-Based Automated Pathology Identification in Retinal Optical Coherence Tomography Images

Ji, Qingge; We, He; Huang, Jie; Sun, Yankui

doi:10.3390/a11060088

Cited by 24 publications

(23 citation statements)

References 42 publications

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“…The algorithm of spatial pyramid matching using sparse coding (ScSPM) [14] utilizes techniques such as SIFT, SC, K-SVD, multi-scale max pooling and linear SVM. The algorithm of deep learning-based CNN (DL-based CNN) [35] removes the last several layers from the pre-trained Inception-v3 and regards the remaining part as a fixed feature extractor. Then the features are used as input of a CNN designed to learn the feature space shifts.…”

Section: Performance Of the Sub-network On Small-scale Datasetmentioning

confidence: 99%

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Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images

Huang

et al. 2019

Algorithms

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Finetuning pre-trained deep neural networks (DNN) delicately designed for large-scale natural images may not be suitable for medical images due to the intrinsic difference between the datasets. We propose a strategy to modify DNNs, which improves their performance on retinal optical coherence tomography (OCT) images. Deep features of pre-trained DNN are high-level features of natural images. These features harm the training of transfer learning. Our strategy is to remove some deep convolutional layers of the state-of-the-art pre-trained networks: GoogLeNet, ResNet and DenseNet. We try to find the optimized deep neural networks on small-scale and large-scale OCT datasets, respectively, in our experiments. Results show that optimized deep neural networks not only reduce computational burden, but also improve classification accuracy.

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Section: Performance Of the Sub-network On Small-scale Datasetmentioning

confidence: 99%

“…Then the features are used as input of a CNN designed to learn the feature space shifts. Results in Table 5 show that all the sub-networks of Inception-v3, ResNet50 and DenseNet121 outperform ScSPM [14], DL-based CNN [35] and IBDL [9].…”

Section: Performance Of the Sub-network On Small-scale Datasetmentioning

confidence: 99%

Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images

Huang

et al. 2019

Algorithms

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“…It utilizes retinal flattening and volume of interest generation. Karri et al [15] and Ji et al [16] employed transfer learning for macular OCT classification where they fine-tuned GoogleNet and InceptionV3 network, respectively.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Dual-Attention Based CNN for Macular Optical Coherence Tomography Classification

Mishra

Mandal

Puhan

2019

IEEE Signal Process. Lett.

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In this letter, we propose a multi-level dual-attention model to classify two common macular diseases, age-related macular degeneration (AMD) and diabetic macular edema (DME) from normal macular eye conditions using optical coherence tomography (OCT) imaging technique. Our approach unifies the dual-attention mechanism at multi-levels of the pre-trained deep convolutional neural network (CNN). It provides a focused learning mechanism by taking into account both multi-level features based attention focusing on the salient coarser features and self-attention mechanism attending higher entropy regions of the finer features. Our proposed method enables the network to automatically focus on the relevant parts of the input images at different levels of feature subspaces. This leads to a more locally deformation-aware feature generation and classification. The proposed approach does not require pre-processing steps such as extraction of region of interest, denoising and retinal flattening, making the network more robust and fully automatic. Experimental results on two macular OCT databases show the superior performance of our proposed approach as compared to the current state-of-the-art methodologies.

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“…Over the past two decades, a majority of works related to optical coherence tomography (OCT) 1,2 retinal image analysis have focused on two fields: segmentation [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] and classification. [22][23][24][25][26][27][28][29][30][31][32][33][34] Most of these works adopt a preprocessing process in order to make images have more attributes, which fit the needs of a follow-up procedure. This process can solve several key issues and prove to be very effective in practice.…”

Section: Introductionmentioning

confidence: 99%

“…Another group aligns a retinal area by detecting the whole retinal region using morphological detection (MD) and fitting a second-order polynomial and/or a straight line to it. 23,27,30 Using MD method to find the main part of a retinal region is a more robust work than segmenting a slender RPE layer, which could wipe out irrelevant areas that follow-up procedures do not need. However, this method also has some problems, which are mainly related to the precise detection of the retinal morphology.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of retinal regions using fully convolutional networks for diagnosis of abnormal maculae in optical coherence tomography images

Sun

2019

J. Biomed. Opt.

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In conventional retinal region detection methods for optical coherence tomography (OCT) images, many parameters need to be set manually, which is often detrimental to their generalizability. We present a scheme to detect retinal regions based on fully convolutional networks (FCN) for automatic diagnosis of abnormal maculae in OCT images. The FCN model is trained on 900 labeled age-related macular degeneration (AMD), diabetic macular edema (DME) and normal (NOR) OCT images. Its segmentation accuracy is validated and its effectiveness in recognizing abnormal maculae in OCT images is tested and compared with traditional methods, by using the spatial pyramid matching based on sparse coding (ScSPM) classifier and Inception V3 classifier on two datasets: Duke dataset and our clinic dataset. In our clinic dataset, we randomly selected half of the B-scans of each class (300 AMD, 300 DME, and 300 NOR) for training classifier and the rest (300 AMD, 300 DME, and 300 NOR) for testing with 10 repetitions. Average accuracy, sensitivity, and specificity of 98.69%, 98.03%, and 99.01% are obtained by using ScSPM classifier, and those of 99.69%, 99.53%, and 99.77% are obtained by using Inception V3 classifier. These two classification algorithms achieve 100% classification accuracy when directly applied to Duke dataset, where all the 45 OCT volumes are used as test set. Finally, FCN model with or without flattening and cropping and its influence on classification performance are discussed. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Efficient Deep Learning-Based Automated Pathology Identification in Retinal Optical Coherence Tomography Images

Cited by 24 publications

References 42 publications

Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images

Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images

Multi-Level Dual-Attention Based CNN for Macular Optical Coherence Tomography Classification

Automatic detection of retinal regions using fully convolutional networks for diagnosis of abnormal maculae in optical coherence tomography images

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