A DEEP LEARNING BASED APPROACH FOR OCT IMAGE QUALITY ASSURANCE
Aspects of the disclosure relate to systems, methods, and algorithms to train a machine learning model or neural network to classify OCT images. The neural network or machine learning model can receive annotated OCT images indicating which portions of the OCT image are blocked and which are clear as...
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| creator | GOPINATH, Ajay SAVIDGE, Kyle, Edward BLABER, Justin, Akira CHEN, Humphrey ZHANG, Angela AMIS, Gregory, Patrick |
| description | Aspects of the disclosure relate to systems, methods, and algorithms to train a machine learning model or neural network to classify OCT images. The neural network or machine learning model can receive annotated OCT images indicating which portions of the OCT image are blocked and which are clear as well as a classification of the OCT image as clear or blocked. After training, the neural network can be used to classify one or more new OCT images. A user interface can be provided to output the results of the classification and summarize the analysis of the one or more OCT images. |
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| subjects | CALCULATING COMPUTING COUNTING DIAGNOSIS HUMAN NECESSITIES HYGIENE IDENTIFICATION IMAGE DATA PROCESSING OR GENERATION, IN GENERAL MEDICAL OR VETERINARY SCIENCE PHYSICS SURGERY |
| title | A DEEP LEARNING BASED APPROACH FOR OCT IMAGE QUALITY ASSURANCE |
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