DEEP LEARNING MODEL OPTIMIZATION METHOD AND APPARATUS FOR MEDICAL IMAGE SEGMENTATION

Disclosed are a deep learning model optimization method and apparatus for medical image segmentation. A deep learning model optimization method for medical image segmentation includes: (a) initializing a model parameter; (b) updating the model parameter by performing model-agnostic meta learning (MA...

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Bibliographic Details
Main Authors KWON, Sun Kyu, KIM, Yeong Joon, MOK, Yeong Heon, KANG, Dong Goo, PAIK, Joon Ki
Format Patent
LanguageEnglish
Published 25.07.2024
Subjects
CALCULATING
COMPUTING
COUNTING
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
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Summary:Disclosed are a deep learning model optimization method and apparatus for medical image segmentation. A deep learning model optimization method for medical image segmentation includes: (a) initializing a model parameter; (b) updating the model parameter by performing model-agnostic meta learning (MAML) on a model based on sample batch and applying a gradient descent algorithm to a loss function; (c) setting an optimizer parameter as the updated model parameter, performing one-shot meta-learning on the model, and then updating the optimizer parameter by applying the gradient descent algorithm to the loss function; and (d) updating the model parameter by reflecting the updated optimizer parameter.
Bibliography:Application Number: US202318398759