Parameter-Efficient Fine-Tuning and Few-Shot Learning of Multiscale Vision Transformers for Liver Tumour Segmentation in CT
Medical Imaging 2025: Computer-Aided Diagnosis, Proceedings of SPIE 13407, 1340738.
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This conference paper is recorded in Medical Imaging 2025: Computer-Aided Diagnosis (2025). This page consolidates verified bibliographic metadata and the external records or research artifacts listed here.
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- Work type
- Conference paper
- Year
- 2025
- Publication date
- 2025-04-04
- Status
- Published
- Venue
- Medical Imaging 2025: Computer-Aided Diagnosis
- Publisher
- SPIE
- Volume
- 13407
- Pages
- 801-810
- Article
- 1340738
- Last verified
- 2026-08-02
Research topics
- parameter-efficient fine-tuning
- few-shot learning
- multiscale vision transformers
- liver tumor segmentation
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Ramtin Mojtahedi, Mohammad Hamghalam, Jacob J. Peoples, William R. Jarnagin, Richard K. G. Do, and Amber L. Simpson. “Parameter-Efficient Fine-Tuning and Few-Shot Learning of Multiscale Vision Transformers for Liver Tumour Segmentation in CT.” Medical Imaging 2025: Computer-Aided Diagnosis, Proceedings of SPIE 13407, 1340738. https://doi.org/10.1117/12.3046253
@inproceedings{Mojtahedi2025PEFTFewShot,
author = {Ramtin Mojtahedi and Mohammad Hamghalam and Jacob J. Peoples and William R. Jarnagin and Richard K. G. Do and Amber L. Simpson},
title = {{Parameter-Efficient Fine-Tuning and Few-Shot Learning of Multiscale Vision Transformers for Liver Tumour Segmentation in CT}},
year = {2025},
booktitle = {Medical Imaging 2025: Computer-Aided Diagnosis},
volume = {13407},
pages = {801-810},
publisher = {SPIE},
doi = {10.1117/12.3046253},
url = {https://doi.org/10.1117/12.3046253}
}
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