Transfer language space with similar domain adaptation: a case study with hepatocellular carcinoma.
- Resource Type
- Article
- Authors
- Tariq, Amara; Kallas, Omar; Balthazar, Patricia; Lee, Scott Jeffery; Desser, Terry; Rubin, Daniel; Gichoya, Judy Wawira; Banerjee, Imon
- Source
- Journal of Biomedical Semantics. 2/23/2022, Vol. 13 Issue 1, p1-12. 12p.
- Subject
- *HEPATOCELLULAR carcinoma
*DEEP learning
*LANGUAGE & languages
*SIGNAL convolution
- Language
- ISSN
- 2041-1480
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. [ABSTRACT FROM AUTHOR]