ArticleJournal of biomedical semantics2022
Transfer language space with similar domain adaptation: a case study with hepatocellular carcinoma.
Article in Journal of biomedical semantics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A foundation systematic review of natural language processing applied to gastroenterology & hepatology.BMC gastroenterology · 2025Pooled it
- Natural language processing and LLMs in liver imaging: a practical review of clinical applications.Abdominal radiology (New York) · 2026Review
- Medical accuracy of artificial intelligence chatbots in oncology: a scoping review.The oncologist · 2025Article
- Utilizing a domain-specific large language model for LI-RADS v2018 categorization of free-text MRI reports: a feasibility study.Insights into imaging · 2024Article
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Authors and funding
8 authors.
Funding
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Abstract
backgroundTransfer 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.
methodWe 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.
resultsWe 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.
conclusionWe 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.
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