META-LEARNING OF PATHOLOGIES FROM RADIOLOGY REPORTS USING VARIANCE-AWARE PROTOTYPICAL NETWORKS

• Main Authors: Abraham, Nabila, Odry, Benjamin, Sehanobish, Arijit, Kannan, Kawshik, Das, Anasuya
• Format: Patent
• Language: English
• Published: 25.01.2024
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS

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.