Systems and methods using weighted-ensemble supervised-learning for automatic detection of ophthalmic disease from images
Disclosed herein are systems, methods, and devices for classifying ophthalmic images according to disease type, state, and stage. The disclosed invention details systems, methods, and devices to perform the aforementioned classification based on weighted-linkage of an ensemble of machine learning models. In some parts, each model is trained on a training data set and tested on a test dataset. In other parts, the models are ranked based on classification performance, and model weights are assigned based on model rank. To classify an ophthalmic image, that image is presented to each model of the ensemble for classification, yielding a probabilistic classification score—of each model. Using the model weights, a weighted-average of the individual model-generated probabilistic scores is computed and used for the classification.
1. A method for weighted-ensemble training of machine-learning models to classify ophthalmic images according to features such as disease type and state; where the method comprises of:
a) an ensemble of machine-learning models each of which consists of:
i. a feature extraction mechanism; and
ii. a classification mechanism;
b) a step to split the input data into training and test sets;
c) a step to initialize the weights;
d) for each model, a step in which the feature extraction mechanism yields a feature vector or other object encoding the ophthalmic image features;
e) for each model, a step in which the feature vector is passed into the classification mechanism to yield a class prediction;
f) for each model, a mechanism to iteratively update the weights to reduce class prediction error;
g) for each model, a stopping mechanism for the iteration;
h) a step to compare and rank the models based on their performance on a test dataset:
i) a step to assign weights to the various models in the ensemble; and
j) given a subject ophthalmic image, a step to compute the weighted-average of the class predictions of the plurality of models, and to choose the ophthalmic image class based on this weighted-averaging step.
2. The method of claim 1 wherein some model of the ensemble is a convolutional neural network.
3. The method of claim 1 wherein some model of the ensemble is a recurrent neural network.
4. The method of claim 1 wherein a rectified linear unit (ReL U) or leaky ReL U is used as the activation function of hidden layers.
5. The method of claim 1 wherein a softmax function is used as the activation function of the output layer.
6. The method of claim 1 wherein batch normalization is performed.
7. The method of claim 1 wherein drop out regularization is performed in the input layers.
8. The method of claim 1 wherein the weight initialization step utilizes a pretrained model.
9. The method of claim 1 wherein the weight initialization step is based on random assignment.
10. The method of claim 1 wherein the iterative weight update mechanism is backpropagation.
11. The method of claim 1 wherein the stopping mechanism is to proceed iteratively till a preset number of iterations or till a preset prediction performance threshold is reached.
12. The method of claim 1 wherein the method for assigning weights to models is based on model performance rank.
13. The method of claim 1 wherein a pooling step is performed between feature extraction or classification layers.
14. A combined imaging and computing stem, consisting:
a) a system to capture or retrieve an ophthalmic image;
b) a computer or computing environment consisting of processing and storage components;
c) a trained weighted-ensemble of machine learning models stored on the storage component; and
d) executable commands stored on the storage component such that, upon command,
i. an ophthalmic image is obtained;
ii. the ophthalmic image is stored in the storage components;
iii. the ophthalmic image is retrieved and classified by passage through the trained weighted-ensemble;
iv. the image class such as disease state and stage is provided as output; and
v. the image class can be transmitted over a network to a third party for storage, further interpretation, and/or appropriate action.
15. The method of claim 14 wherein the ophthalmic image is obtained by an integrated local device which captures the image of an eye or some of its parts in real time.
16. The method of claim 14 wherein the ophthalmic image is obtained by retrieval from a remote imaging system or database.
17. The method of claim 14 wherein some of the models in the ensemble are convolutional neural networks.
18. The method of claim 14 wherein some of the models in the ensemble are recurrent neural networks.
19. The method of claim 14 wherein the trained weighted-ensemble is trained as follows:
a) a database of labeled ophthalmic images is split into training and test sets;
b) each model in the ensemble is trained and tested;
c) the models are ranked based on their performance on the test dataset; and
d) a model weight is assigned to each model based on its performance rank.
20. The method of claim 19 wherein classification of an ophthalmic image is done as follows:
a) the image is passed through each model, generating probabilistic class scores for each;
b) using the model weights, a weighted-average of the probabilistic class scores is computed across models; and
c) the weighted-average of class scores is used to classify the image.