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NAISTSOCRR at the NTCIR-17 MedNLP-SC Radiology Report Subtask
0
Zitationen
5
Autoren
2023
Jahr
Abstract
This paper describes how we tackled the Medical Natural Language Processing for Radiology Report TNM staging (RR-TNM) Subtask as participants of NTCIR17. The RR-TNM Subtask is a MedNLP-SC original task to classify radiology reports under multiple criteria. We introduced three different methods based on pre-trained language models (PLMs), including a medical-specific model. Notably, our combination approach, utilizing JMedRoBERTa (manbyo-wordpiece) for label T, Tohoku-BERT-v3 for label N, and UTH-BERT for label M, achieved an accuracy of 0.3704 on the test data. This performance was the highest among all participants, emphasizing the effectiveness of our strategy.
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