Towards integrating domain knowledge, AutoML and few-shot learning for medical image analysis: a mini review of current trends and research gaps
- DOI
- 10.1007/s12194-026-01077-3
- Published
- 2026-06-06
- Container
- Radiological Physics and Technology
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s12194-026-01077-3,
title = {Towards integrating domain knowledge, AutoML and few-shot learning for medical image analysis: a mini review of current trends and research gaps},
author = {Nitiyaa Ragu and Jason Teo},
year = {2026},
journal = {Radiological Physics and Technology},
doi = {10.1007/s12194-026-01077-3},
url = {https://doi.org/10.1007/s12194-026-01077-3}
}RIS
TY - JOUR TI - Towards integrating domain knowledge, AutoML and few-shot learning for medical image analysis: a mini review of current trends and research gaps AU - Nitiyaa Ragu AU - Jason Teo PY - 2026 JO - Radiological Physics and Technology DO - 10.1007/s12194-026-01077-3 UR - https://doi.org/10.1007/s12194-026-01077-3 ER -
APA
Ragu, N., & Teo, J. (2026). Towards integrating domain knowledge, AutoML and few-shot learning for medical image analysis: a mini review of current trends and research gaps. Radiological Physics and Technology. https://doi.org/10.1007/s12194-026-01077-3
Source records
- crossref · retrieved 2026-09-25T06:11:06.115Z