Abstract
Background: Transfer learning is a common practice in image classification with deep learning where the available data is often limited for training a complex model with millions of parameters. However, transferring language models requires special attention since cross-domain vocabularies (e.g. between two different modalities MR and US) do not always overlap as the pixel intensity range overlaps mostly for images. Method: We present a concept of similar domain adaptation where we transfer inter-institutional language models (context-dependent and context-independent) between two different modalities (ultrasound and MRI) to capture liver abnormalities. Results: We use MR and US screening exam reports for hepatocellular carcinoma as the use-case and apply the transfer language space strategy to automatically label imaging exams with and without structured template with > 0.9 average f1-score. Conclusion: We conclude that transfer learning along with fine-tuning the discriminative model is often more effective for performing shared targeted tasks than the training for a language space from scratch.
Original language | English (US) |
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Article number | 8 |
Journal | Journal of Biomedical Semantics |
Volume | 13 |
Issue number | 1 |
DOIs | |
State | Published - Dec 2022 |
Keywords
- BERT
- Language model
- Radiology report
- Transfer learning
- Word2vec
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Health Informatics
- Computer Networks and Communications