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

Japanese

深層学習モデルは、地域を通じて直接医療データイメージングで不可欠なツールとなっていますが、様々なタスクのためのエンド-to-エンドの訓練は、限定されたラベルデータと計算制約のために課題が残っています。これに対処するために、我々はBrain2Vec、74,425 T1の重量化磁気共鳴イメージング(MRI)スキャンで事前に訓練された基盤モデルを導入します。Brain2Vecは、重量のイニシアライゼーションのバックボンとして機能し、ダウンストリーム予測タスクの最後のタスクのためのインスタンディングと独立した機能エキストラとして機能します。我々は、ダウンストリーム予測パフォーマンス、コンバーゲンススピード、および組み込み品質

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.

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

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