META-LEARNING OF PATHOLOGIES FROM RADIOLOGY REPORTS USING VARIANCE-AWARE PROTOTYPICAL NETWORKS
A process can include performing meta-learning for a variance-aware prototypical network pre-trained on a dataset comprising examples of a first type of radiology report associated with a single domain. The meta-learning comprises learning one or more prototype representations for each radiology cla...
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Main Authors | , , , , |
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Format | Patent |
Language | English |
Published |
25.01.2024
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Subjects | |
Online Access | Get full text |
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Summary: | A process can include performing meta-learning for a variance-aware prototypical network pre-trained on a dataset comprising examples of a first type of radiology report associated with a single domain. The meta-learning comprises learning one or more prototype representations for each radiology classification task and a variance information for the prototype representations of each radiology classification task. The one or more respective prototype representations for each radiology classification task are modeled as a Gaussian and a query sample comprising text data of a type of radiology report seen during the meta-learning is provided to the variance-aware prototypical network. A distance metric is determined between a Dirac distribution representation of the query sample and the Gaussians of the respective prototype representations for each radiology classification task included in the meta-learning. The query sample is classified based on identifying a respective prototype representation having the smallest distance metric. |
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Bibliography: | Application Number: US202318226008 |