Newest papers

Brain2Vec: A Self-Supervised 3D Foundation Model for Structural Brain MRI

Spiros Maggioros, Güray Erus, Gareth A. Harman, George Aidinis, Alexander Getka, Aristeidis Sotiras, Χρήστος Δαβατζίκος · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28411 · cited by 0 works (OpenAlex) · open-access PDF

Portuguese

Os modelos de aprendizagem profunda através de regiões se tornaram ferramentas essenciais na imagem de dados médicos, mas treiná-los de ponta a ponta para várias tarefas permanece desafiador devido a limitados dados rotulados e constrangimentos computacionais. Para abordar isso, apresentamos o Brain2Vec, um modelo de fundação pré-treinado em 74,425 T1 ponderadas imagens de ressonância magnética (MRI) de desempenho preditivo, velocidade de convergência e qualidade de incorporação. O Brain2Vec pode servir tanto como um backbone de inicialização de peso como um backbone de instalação finamente ajustado para tarefas de previsão de idade do cérebro e como um extractor de recursos independentes. Nós avaliamos o Brain2Vec em três tarefas:

Machine translation (Meta M2M100); the English original follows.

Abstract

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 quality. Using Brain2Vec's pretrained weights as initialization and fine-tuning on labeled data for a brain age prediction task, results demonstrate improved final accuracy and faster convergence compared to random initialization. Additionally, its frozen embeddings outperform anatomically defined volumetric features on four of five downstream classification tasks and perform competitively with a state-of-the-art foundation model pre-trained on substantially more data. Lastly, we show that Brain2Vec encodes information primarily from a spatially localized and neuroanatomically meaningful set of brain regions, and that manipulating a subset of latent features produces monotonic, region-specific effects on the reconstructed anatomy. Brain2Vec code and trained models are available as an open-source package on https://github.com/CBICA/Brain2Vec. Users can apply Brain2Vec locally by installing the package, or directly through the NiChart cloud platform https://cloud.neuroimagingchart.com, where Brain2Vec features can be derived without requiring local installation or specialized infrastructure.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Initialization, Computer science, Downstream (manufacturing), Artificial intelligence, Set (abstract data type), Feature (linguistics)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange