Spiros Maggioros, Guray Erus, Gareth Harman, George Aidinis, Alexander Getka, Aristeidis Sotiras, Christos Davatzikos · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28411 · open-access PDF
Model pembelajaran mendalam telah menjadi alat penting dalam pencitraan medis, namun pelatihan mereka end-to-end untuk tugas yang beragam tetap menantang karena data yang terbatas dan keterbatasan komputasi. Untuk mengatasi ini, kami memperkenalkan Brain2Vec, model dasar yang telah dilatih sebelumnya pada 74.425 T1-berat magnetic resonance imaging (MRI) scans dikumpulkan dari beberapa studi. Brain2Vec dapat berfungsi sebagai tulang belakang inisialisasi berat untuk menyelaraskan tugas downstream dan sebagai ekstraktor fitur mandiri. Kami mengevaluasi Brain2Vec dalam tiga tugas: kinerja prediksi downstream, kecepatan konvergensi, dan embedding quo
Machine translation (Meta M2M100); the English original follows.
Deep learning models have become essential tools in medical imaging, yet training them end-to-end for diverse tasks remains challenging due to limited labeled data and computational constraints. To address this, we introduce Brain2Vec, a foundation model pre-trained on 74,425 T1-weighted Magnetic Resonance Imaging (MRI) scans pooled from multiple studies. Brain2Vec can serve both as a weight initialization backbone for fine-tuning downstream tasks and as a standalone feature extractor. We evaluated Brain2Vec in three tasks: downstream predictive performance, convergence speed, and embedding qu
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