Self-supervised 3D Vision Transformer Pre-training for Robust Brain Tumor Classification
- Brain tumors pose significant challenges in neurology, making precise classification crucial for prognosis and treatment planning. This work investigates the effectiveness of a self-supervised learning approach–masked autoencoding (MAE)–to pre-train a vision transformer (ViT) model for brain tumor classification. Our method uses non-domain specific data, leveraging the ADNI and OASIS-3 MRIBrain tumors pose significant challenges in neurology, making precise classification crucial for prognosis and treatment planning. This work investigates the effectiveness of a self-supervised learning approach–masked autoencoding (MAE)–to pre-train a vision transformer (ViT) model for brain tumor classification. Our method uses non-domain specific data, leveraging the ADNI and OASIS-3 MRI datasets, which primarily focus on degenerative diseases, for pretraining. The model is subsequently fine-tuned and evaluated on the BraTS glioma and meningioma datasets, representing a novel use of these datasets for tumor classification. The pre-trained MAE ViT model achieves an average F1 score of 0.91 in a 5-fold cross-validation setting, outperforming the nnU-Net encoder trained from scratch, particularly under limited data conditions. These findings highlight the potential of self-supervised MAE in enhancing brain tumor classification accuracy, even with restricted labeled data.…


| Author: | Danilo Weber Nunes, David Rauber, Christoph PalmORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1007/978-3-658-47422-5_69 |
| Parent Title (German): | Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025 |
| Publisher: | Springer Vieweg |
| Place of publication: | Wiesbaden |
| Editor: | Christoph PalmORCiDGND, Katharina Breininger, Thomas M. DesernoORCiD, Heinz HandelsORCiD, Andreas MaierORCiD, Klaus H. Maier-HeinORCiD, Thomas Tolxdorff |
| Document Type: | conference proceeding (article) |
| Language: | English |
| Year of first Publication: | 2025 |
| Release Date: | 2025/04/28 |
| First Page: | 298 |
| Last Page: | 303 |
| Andere Schriftenreihe: | Informatik aktuell |
| Institutes: | Fakultät Informatik und Mathematik |
| Regensburg Center of Health Sciences and Technology - RCHST | |
| Fakultät Informatik und Mathematik / Labor Regensburg Medical Image Computing (ReMIC) | |
| Begutachtungsstatus: | peer-reviewed |
| research focus: | Lebenswissenschaften und Ethik |
| Licence (German): |


