SYSTEMS AND METHODS FOR FEATURE DETECTION IN RETINAL IMAGES
First Claim
1. A method for training a neural network to detect features in a retinal image comprising:
- a) extracting one or more Features Images from a Train_0 set, a Test_0set, a Train_1 set and a Test_1 set;
b) combining and randomizing the Feature Images from Train_0 and Train_1 into a training data set;
c) combining and randomizing the Feature Images from Test_0 and Test_1into a testing dataset;
d) training a plurality of neural networks having different architectures using a subset of the training dataset while testing on a subset of the testing dataset;
e) identifying the best neural network based on each of the plurality of neural networks performance on the testing data set;
f) inputting images from Test_0, Train_1, Train_0 and Test_1 to the best neural network and identifying a limited number of false positives and false negative and adding the false positives and false negatives to the training dataset and testing dataset; and
g) repeating steps d)-g) until an objective performance threshold is reached.
3 Assignments
0 Petitions
Accused Products
Abstract
Provide are systems methods and devices for diagnosing disease in medical images. In certain aspects, disclosed is a method for training a neural network to detect features in a retinal image including the steps of: a) extracting one or more features images from a Train_0 set, a Test_0 set, a Train_1 set and a Test_1 set; b) combining and randomizing the feature images from Train_0 and Train_1 into a Training data set; c) combining and randomizing the feature images from Test_0 and Test_1 into a testing dataset; d) training a plurality of neural networks having different architectures using a subset of the training dataset while testing on a subset of the testing dataset; e) identifying the best neural network based on each of the plurality of neural networks performance on the testing data set; f) inputting images from Test_0, Train_1, Train_0 and Test_1 to the best neural network and identifying a limited number of false positives and false negative and adding the false positives and false negatives to the training dataset and testing dataset; and g) repeating steps d)-g) until an objective performance threshold is reached.
73 Citations
20 Claims
-
1. A method for training a neural network to detect features in a retinal image comprising:
-
a) extracting one or more Features Images from a Train_0 set, a Test_0set, a Train_1 set and a Test_1 set; b) combining and randomizing the Feature Images from Train_0 and Train_1 into a training data set; c) combining and randomizing the Feature Images from Test_0 and Test_1into a testing dataset; d) training a plurality of neural networks having different architectures using a subset of the training dataset while testing on a subset of the testing dataset; e) identifying the best neural network based on each of the plurality of neural networks performance on the testing data set; f) inputting images from Test_0, Train_1, Train_0 and Test_1 to the best neural network and identifying a limited number of false positives and false negative and adding the false positives and false negatives to the training dataset and testing dataset; and g) repeating steps d)-g) until an objective performance threshold is reached. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
-
-
19. A computing device for diagnosing disease in a retinal image comprising:
-
a) a processor; b) a memory that comprises; i) a feature extractor system ii) a neural network; iii) a machine learning program; and iv) instructions that, when executed by the processor, cause the processor to perform acts comprising; receiving a retinal image and providing the retinal image to the feature extractor; extracting image features and providing image features to the neural network; generating scalar features from the image features; providing scalar features to the machine learning program; and determining whether disease is present in the retinal image. - View Dependent Claims (20)
-
Specification