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A publicly available dataset of OCT images from the Cell dataset[9] and BOE dataset[10] was selected as the auxiliary dataset for training. The Cell dataset contains four classifications, including choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal, which total 109,309 samples with two image resolutions, 1,536 × 496 and 1,024 × 496 pixels. The BOE dataset has 3,231 samples, including dry AMD, DME, and normal, with three types of image resolution: 1,024 × 496, 768 × 496, and 512 × 496 pixels.
SD-OCT images of the target dataset were acquired using the Cirrus HD-OCT 5000 system (Carl Zeiss Meditec Inc., Dublin, USA) and the Heidelberg Spectralis system (Heidelberg Engineering, Heidelberg, Germany). An OCT scan containing the center of the fovea from each macular OCT was selected as the input data. Images from the Cirrus system were scanned at a length of 6 mm, and the image resolution was 1,180 × 786 pixels. The images from the Spectralis system were scanned at a length of 6 or 9 mm, and the image resolution was 1,024 × 496 and 1,536 × 496 pixels, respectively.
Two certified retinal specialists graded all OCT images in the clinical datasets separately. The diagnosis of IRDs was based on both clinical and genetic detections. The OCT images of IRDs were classified into three types based on morphological features: cone/cone-rod lesions, that is, disruption of photoreceptor layers with thinned sensory retina at the fovea; rod-cone lesions, that is, disruption of photoreceptor layers with thinned sensory retina at the areas outside the fovea with relatively preserved structure at the fovea; and extensive lesions, that is, extensive disruption of photoreceptor layers with thinned sensory retina (Figure 1).
Figure 1. OCT images of three categories in IRDs. (A) Cone/cone-rod lesions: disruption of photoreceptor layers with thinned sensory retina at the fovea. (B) Rod-cone lesions: disruption of photoreceptor layers with thinned sensory retina at the areas outside the fovea with relatively preserved structure at the fovea. (C) Extensive lesions: extensive disruption of photoreceptor layers with thinned sensory retina. IRDs: inherited retinal disorders.
This study was conducted following the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Beijing Tongren Hospital, Capital Medical University.
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The proposed pipeline consisted of three parts: data preprocessing, training the teacher model, and training the student model. Data preprocessing mainly includes image angle adjustments and vertical pixel column movements. The teacher model was first trained on the auxiliary OCT datasets with a four-class classification designed to learn the nuances of each disease and then transferred to the target OCT dataset to classify the five target classes. The student model was trained using the soft label provided by the teacher model based on knowledge distillation (KD) [11] and the hard label from annotation (Figure 2).
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The original OCT images show different angles, noise distribution, and size diversity because of the acquisition machine and the patient. This will distract the neural network from the focal area and increase the training time due to useless data input during training. We used a random inversion of the images in the process, which did not destroy the hierarchical information of the OCT images, and cropped the given images to a random size and aspect ratio to increase the generalization ability of the model. The experiments were all preprocessed and resized to 224 × 224 pixels.
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The ResNet-50, a convolutional neural network (CNN) framework, was chosen as the teacher model, which is the backbone structure for absorbing and learning the information from the auxiliary dataset, specifically the textures, patterns, and pixel distributions in the end-level convolutional layers. After learning using the same type of dataset, we froze the parameters of the teacher model, except for the last fully connected layer, to transfer the task to the target domain.
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To overcome the obstacles caused by the lack of training data, we used the combination of KD and student–teacher learning[12] for knowledge transfer. ResNet-18 was used as the student model to be trained from scratch to adapt to the target data with a smaller number of samples.
$$ \begin{aligned} {\mathcal{L}} (x;W)=& \alpha \cdot H\left(y,\sigma \left({z}_{s};T=1\right)\right)+\beta \\ & \cdot H\left(\sigma \left({z}_{t};T=\tau \right),\sigma \left({z}_{s};T=\tau \right)\right) \end{aligned}$$ (1) where in Equation 1, α and
$\beta $ control the balance of the information coming from the two sources, which generally add up to 1. H is the loss function, σ is the softmax function parameterized by the temperature T, zs is the logits from the student network, and zt is the logits from the teacher network. τ denotes the temperature of the adapted softmax function, and each probability pi of class i in the batch is calculated from logits zi as follows:$$ {p}_{i}=\frac{\mathrm{exp}\left(\frac{{z}_{i}}{T}\right)}{\sum _{j} \mathrm{e}\mathrm{x}\mathrm{p}\left(\frac{{z}_{j}}{T}\right)} $$ (2) where T in Equation 2 increases, the probability distribution of the output becomes “softer,” which means that the differences among the probabilities of each class decrease and more information will be provided.
Figure 3 illustrates the network architecture of the model. The model was trained and tested using the Python (version 3.10.7) programming language with the PyTorch (version 1.8.1) library as the backend. The computer used in this study was equipped with an NVIDIA GeForce RTX 2070 8 GB graphics processing unit, 32 GB random access memory, and an Intel Core 11th Gen Intel(R) Core(TM) i9-11900 @ 2.50GHz central processing unit.
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To fully evaluate the performance of the proposed method, we used various evaluation metrics, namely accuracy, sensitivity, specificity, and F1 score, which are defined as follows:
$$ \text{Accuracy}=\frac{TP+TN}{TP+TN+FN+FP} $$ (3) $$ \text{Sensitivity}\text{}=\frac{TP}{TP+FN} $$ (4) $$ \text{Specificity}\text{}=\frac{TN}{TN+FP} $$ (5) $$ \mathrm{F}1=\frac{2TP}{2 {TP}+FN+FP} $$ (6) where TP, TN, FP, and FN denote true positives, true negatives, false positives, and false negatives, respectively, and are measured according to the confusion matrix.
Receiver operating characteristic (ROC) curves were also used to display the performance of the FSL model for the classification of OCT images. Heatmaps were used to show the regions of interest (ROI) in the model.
The process was repeated three times with random assignment of participants to the training and testing sets to control for selection bias, given the relatively small sample size. The metrics were calculated for each training/testing process for each category.
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A total of 2,317 images from 189 participants were included in this study as the target dataset, of which 1,126 images of 79 participants were IRDs, 533 images of 43 participants were normal samples, and 658 images of 67 participants were controls, including CNV, DME, macular hole, epiretinal membrane, and retinal detachment (Table 1). The images were randomly split in a 3:1 ratio into the training and testing sets.
Table 1. Composition of the target dataset
Variables Cirrus system Spectralis system Total Subjects Images Subjects Images Subjects Images IRDs 41 739 38 387 79 1,126 Cone/cone-rod lesions 17 271 5 130 22 401 Rod-cone lesions 12 222 9 144 21 366 Extensive lesions 12 246 24 113 36 359 Normal 25 265 18 268 43 533 Control 35 311 32 347 67 658 Total 101 1,315 88 1,002 189 2,317 Note. IRDs: inherited retinal disorders. -
The FSL model achieved better performance than the baseline model. The baseline model was adapted from ResNet-18, which has the same structure as the student model. The baseline model was added to demonstrate that excluding the advantages of the model performance itself, our proposed method is the main contributor to improving the final results. In the three testing sets, the FSL model achieved a total accuracy ranging from 0.974 [95% confidence interval (CI) 0.968–0.979] to 0.983 (95% CI 0.978–0.987), total sensitivity from 0.934 (95% CI 0.911–0.952) to 0.957 (95% CI 0.936–0.971), total specificity from 0.984 (95% CI 0.978–0.988) to 0.990 (95% CI 0.984–0.993), and total F1 score from 0.935 (95% CI 0.913–0.954) to 0.957 (95% CI 0.938–0.972). The baseline model achieved total accuracy ranging from 0.943 (95% CI 0.934–0.951) to 0.954 (95% CI 0.946–0.961), total sensitivity from 0.866 (95% CI 0.835–0.892) to 0.886 (95% CI 0.856–0.909), total specificity from 0.962 (95% CI 0.954–0.969) to 0.971 (95% CI 0.963–0.977), and total F1 score from 0.859 (95% CI 0.828–0.885) to 0.885 (95% CI 0.857–0.909) (Table 2).
Table 2. Summary of model performance in the classification of OCT images
Variables FSL model Baseline model Accuracy
(95% CI)Sensitivity
(95% CI)Specificity
(95% CI)F1 Score
(95% CI)Accuracy
(95% CI)Sensitivity
(95% CI)Specificity
(95% CI)F1 Score
(95% CI)Test1 (n = 599) Test1 (n = 599) IRDs Cone/cone-rod lesions 0.975
(0.958–0.985)0.988
(0.927–0.999)0.973
(0.954–0.984)0.919
(0.844–0.966)0.939
(0.916–0.957)0.860
(0.794–0.907)0.970
(0.947–0.983)0.887
(0.825–0.930)Rod-cone lesions 0.992
(0.980–0.9970.984
(0.939–0.997)0.994
(0.980–0.998)0.981
(0.939–0.997)0.934
(0.910–0.952)0.586
(0.476–0.689)0.994
(0.981–0.998)0.723
(0.597–0.816)Extensive lesions 0.967
(0.948–0.979)0.855
(0.774–0.911)0.994
(0.980–0.998)0.909
(0.835–0.953)0.954
(0.934–0.969)0.967
(0.913–0.990)0.951
(0.927–0.968)0.897
(0.833–0.944)Normal 0.995
(0.984–0.999)0.990
(0.935–0.999)0.996
(0.984–0.999)0.984
(0.935–0.999)0.966
(0.948–0.979)0.933
(0.854–0.972)0.972
(0.953–0.984)0.892
(0.807–0.944)Control 0.987
(0.973–0.994)0.971
(0.930–0.989)0.993
(0.978–0.998)0.977
(0.937–0.993)0.944
(0.922–0.961)0.947
(0.889–0.976)0.943
(0.918–0.962)0.883
(0.818–0.931)Total 0.983
(0.978–0.987)0.957
(0.936–0.971)0.990
(0.984–0.993)0.957
(0.938–0.972)0.948
(0.939–0.955)0.872
(0.842–0.897)0.967
(0.958–0.973)0.870
(0.840–0.896)Test2 (n = 610) Test2 (n = 610) IRDs Cone/cone-rod lesions 0.967
(0.949–0.979)0.954
(0.890–0.983)0.970
(0.950–0.983)0.912
(0.839–0.954)0.943
(0.921–0.960)0.804
(0.713–0.872)0.973
(0.954–0.985)0.835
(0.746–0.894)Rod-cone lesions 0.993
(0.982–0.998)0.984
(0.905–0.999)0.995
(0.983–0.999)0.969
(0.884–0.995)0.955
(0.934–0.970)0.767
(0.637–0.862)0.976
(0.958–0.986)0.773
(0.650–0.873)Extensive lesions 0.967
(0.949–0.9790.839
(0.745–0.904)0.990
(0.976–0.996)0.886
(0.796–0.941)0.948
(0.926–0.964)0.864
(0.782–0.919)0.967
(0.946–0.980)0.860
(0.809–0.938)Normal 0.980
(0.965–0.989)0.899
(0.823–0.946)0.998
(0.987–0.999)0.942
(0.874–0.976)0.941
(0.919–0.958)0.849
(0.763–0.909)0.961
(0.939–0.975)0.837
(0.755–0.902)Control 0.962
(0.943–0.975)0.966
(0.932–0.984)0.960
(0.933–0.977)0.952
(0.917–0.976)0.928
(0.904–0.947)0.935
(0.890–0.962)0.924
(0.892–0.948)0.903
(0.857–0.939)Total 0.974
(0.968–0.979)0.934
(0.911–0.952)0.984
(0.978–0.988)0.935
(0.913–0.954)0.943
(0.934–0.951)0.866
(0.835–0.892)0.962
(0.954–0.969)0.859
(0.828–0.885)Test3 (n = 594) Test3 (n = 594) IRDs Cone/cone-rod Lesions 0.988
(0.975–0.995)0.987
(0.922–0.999)0.988
(0.973–0.995)0.957
(0.888–0.990)0.958
(0.938–0.972)0.816
(0.730–0.880)0.992
(0.977–0.997)0.882
(0.805–0.937)Rod-Cone Lesions 0.992
(0.979–0.997)0.979
(0.918–0.996)0.994
(0.981–0.998)0.974
(0.918–0.996)0.985
(0.970–0.993)0.933
(0.830–0.978)0.991
(0.977–0.997)0.926
(0.830–0.978)Extensive Lesions 0.983
(0.968–0.991)0.906
(0.786–0.965)0.991
(0.977–0.997)0.906
(0.786–0.965)0.983
(0.968–0.991)0.929
(0.855–0.969)0.994
(0.981–0.998)0.948
(0.878–0.981)Normal 0.980
(0.964–0.989)0.899
(0.823–0.946)0.997
(0.987–0.999)0.942
(0.876–0.976)0.941
(0.918–0.958)0.827
(0.752–0.884)0.976
(0.956–0.987)0.868
(0.799–0.921)Control 0.968
(0.950–0.980)0.957
(0.913–0.980)0.973
(0.951–0.986)0.949
(0.907–0.976)0.902
(0.875–0.925)0.934
(0.885–0.964)0.888
(0.853–0.916)0.854
(0.796–0.899)Total 0.982
(0.977–0.987)0.948
(0.924–0.965)0.989
(0.984–0.993)0.949
(0.926–0.966)0.954
(0.946–0.961)0.886
(0.856–0.909)0.971
(0.963–0.977)0.885
(0.857–0.909)Note. IRDs: inherited retinal disorders; FSL: few-shot learning; CI: confidence interval. -
The performance of the FSL model was compared with that of retinal specialists using ROC curve plots. The AUCs of the FSL model were higher for most sub-classifications (Figure 4).
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The heatmaps demonstrated that the FSL model made the classification based on the correct lesion for diagnosis (Figure 5).
Figure 5. Representative OCT images and corresponding heatmaps. (A) Example of cone/cone-rod lesions of IRDs and its corresponding superimposed heatmap. (B) Example of rod-cone lesions of IRDs and its corresponding superimposed heatmap. (C) Example of extensive lesions of IRDs and its corresponding superimposed heatmap. (D) Example of normal and its corresponding superimposed heatmap. (E) Example of control and its corresponding superimposed heatmap. IRDs: inherited retinal disorders.
doi: 10.3967/bes2023.052
Automated Classification of Inherited Retinal Diseases in Optical Coherence Tomography Images Using Few-shot Learning
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Abstract:
Objective To develop a few-shot learning (FSL) approach for classifying optical coherence tomography (OCT) images in patients with inherited retinal disorders (IRDs). Methods In this study, an FSL model based on a student–teacher learning framework was designed to classify images. 2,317 images from 189 participants were included. Of these, 1,126 images revealed IRDs, 533 were normal samples, and 658 were control samples. Results The FSL model achieved a total accuracy of 0.974–0.983, total sensitivity of 0.934–0.957, total specificity of 0.984–0.990, and total F1 score of 0.935–0.957, which were superior to the total accuracy of the baseline model of 0.943–0.954, total sensitivity of 0.866–0.886, total specificity of 0.962–0.971, and total F1 score of 0.859–0.885. The performance of most subclassifications also exhibited advantages. Moreover, the FSL model had a higher area under curves (AUC) of the receiver operating characteristic (ROC) curves in most subclassifications. Conclusion This study demonstrates the effective use of the FSL model for the classification of OCT images from patients with IRDs, normal, and control participants with a smaller volume of data. The general principle and similar network architectures can also be applied to other retinal diseases with a low prevalence. -
Figure 1. OCT images of three categories in IRDs. (A) Cone/cone-rod lesions: disruption of photoreceptor layers with thinned sensory retina at the fovea. (B) Rod-cone lesions: disruption of photoreceptor layers with thinned sensory retina at the areas outside the fovea with relatively preserved structure at the fovea. (C) Extensive lesions: extensive disruption of photoreceptor layers with thinned sensory retina. IRDs: inherited retinal disorders.
Figure 5. Representative OCT images and corresponding heatmaps. (A) Example of cone/cone-rod lesions of IRDs and its corresponding superimposed heatmap. (B) Example of rod-cone lesions of IRDs and its corresponding superimposed heatmap. (C) Example of extensive lesions of IRDs and its corresponding superimposed heatmap. (D) Example of normal and its corresponding superimposed heatmap. (E) Example of control and its corresponding superimposed heatmap. IRDs: inherited retinal disorders.
Table 1. Composition of the target dataset
Variables Cirrus system Spectralis system Total Subjects Images Subjects Images Subjects Images IRDs 41 739 38 387 79 1,126 Cone/cone-rod lesions 17 271 5 130 22 401 Rod-cone lesions 12 222 9 144 21 366 Extensive lesions 12 246 24 113 36 359 Normal 25 265 18 268 43 533 Control 35 311 32 347 67 658 Total 101 1,315 88 1,002 189 2,317 Note. IRDs: inherited retinal disorders. Table 2. Summary of model performance in the classification of OCT images
Variables FSL model Baseline model Accuracy
(95% CI)Sensitivity
(95% CI)Specificity
(95% CI)F1 Score
(95% CI)Accuracy
(95% CI)Sensitivity
(95% CI)Specificity
(95% CI)F1 Score
(95% CI)Test1 (n = 599) Test1 (n = 599) IRDs Cone/cone-rod lesions 0.975
(0.958–0.985)0.988
(0.927–0.999)0.973
(0.954–0.984)0.919
(0.844–0.966)0.939
(0.916–0.957)0.860
(0.794–0.907)0.970
(0.947–0.983)0.887
(0.825–0.930)Rod-cone lesions 0.992
(0.980–0.9970.984
(0.939–0.997)0.994
(0.980–0.998)0.981
(0.939–0.997)0.934
(0.910–0.952)0.586
(0.476–0.689)0.994
(0.981–0.998)0.723
(0.597–0.816)Extensive lesions 0.967
(0.948–0.979)0.855
(0.774–0.911)0.994
(0.980–0.998)0.909
(0.835–0.953)0.954
(0.934–0.969)0.967
(0.913–0.990)0.951
(0.927–0.968)0.897
(0.833–0.944)Normal 0.995
(0.984–0.999)0.990
(0.935–0.999)0.996
(0.984–0.999)0.984
(0.935–0.999)0.966
(0.948–0.979)0.933
(0.854–0.972)0.972
(0.953–0.984)0.892
(0.807–0.944)Control 0.987
(0.973–0.994)0.971
(0.930–0.989)0.993
(0.978–0.998)0.977
(0.937–0.993)0.944
(0.922–0.961)0.947
(0.889–0.976)0.943
(0.918–0.962)0.883
(0.818–0.931)Total 0.983
(0.978–0.987)0.957
(0.936–0.971)0.990
(0.984–0.993)0.957
(0.938–0.972)0.948
(0.939–0.955)0.872
(0.842–0.897)0.967
(0.958–0.973)0.870
(0.840–0.896)Test2 (n = 610) Test2 (n = 610) IRDs Cone/cone-rod lesions 0.967
(0.949–0.979)0.954
(0.890–0.983)0.970
(0.950–0.983)0.912
(0.839–0.954)0.943
(0.921–0.960)0.804
(0.713–0.872)0.973
(0.954–0.985)0.835
(0.746–0.894)Rod-cone lesions 0.993
(0.982–0.998)0.984
(0.905–0.999)0.995
(0.983–0.999)0.969
(0.884–0.995)0.955
(0.934–0.970)0.767
(0.637–0.862)0.976
(0.958–0.986)0.773
(0.650–0.873)Extensive lesions 0.967
(0.949–0.9790.839
(0.745–0.904)0.990
(0.976–0.996)0.886
(0.796–0.941)0.948
(0.926–0.964)0.864
(0.782–0.919)0.967
(0.946–0.980)0.860
(0.809–0.938)Normal 0.980
(0.965–0.989)0.899
(0.823–0.946)0.998
(0.987–0.999)0.942
(0.874–0.976)0.941
(0.919–0.958)0.849
(0.763–0.909)0.961
(0.939–0.975)0.837
(0.755–0.902)Control 0.962
(0.943–0.975)0.966
(0.932–0.984)0.960
(0.933–0.977)0.952
(0.917–0.976)0.928
(0.904–0.947)0.935
(0.890–0.962)0.924
(0.892–0.948)0.903
(0.857–0.939)Total 0.974
(0.968–0.979)0.934
(0.911–0.952)0.984
(0.978–0.988)0.935
(0.913–0.954)0.943
(0.934–0.951)0.866
(0.835–0.892)0.962
(0.954–0.969)0.859
(0.828–0.885)Test3 (n = 594) Test3 (n = 594) IRDs Cone/cone-rod Lesions 0.988
(0.975–0.995)0.987
(0.922–0.999)0.988
(0.973–0.995)0.957
(0.888–0.990)0.958
(0.938–0.972)0.816
(0.730–0.880)0.992
(0.977–0.997)0.882
(0.805–0.937)Rod-Cone Lesions 0.992
(0.979–0.997)0.979
(0.918–0.996)0.994
(0.981–0.998)0.974
(0.918–0.996)0.985
(0.970–0.993)0.933
(0.830–0.978)0.991
(0.977–0.997)0.926
(0.830–0.978)Extensive Lesions 0.983
(0.968–0.991)0.906
(0.786–0.965)0.991
(0.977–0.997)0.906
(0.786–0.965)0.983
(0.968–0.991)0.929
(0.855–0.969)0.994
(0.981–0.998)0.948
(0.878–0.981)Normal 0.980
(0.964–0.989)0.899
(0.823–0.946)0.997
(0.987–0.999)0.942
(0.876–0.976)0.941
(0.918–0.958)0.827
(0.752–0.884)0.976
(0.956–0.987)0.868
(0.799–0.921)Control 0.968
(0.950–0.980)0.957
(0.913–0.980)0.973
(0.951–0.986)0.949
(0.907–0.976)0.902
(0.875–0.925)0.934
(0.885–0.964)0.888
(0.853–0.916)0.854
(0.796–0.899)Total 0.982
(0.977–0.987)0.948
(0.924–0.965)0.989
(0.984–0.993)0.949
(0.926–0.966)0.954
(0.946–0.961)0.886
(0.856–0.909)0.971
(0.963–0.977)0.885
(0.857–0.909)Note. IRDs: inherited retinal disorders; FSL: few-shot learning; CI: confidence interval. -
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