Accurate prediction of disease-risk factors from volumetric medical scans by a deep vision model pre-trained with 2D scans.

ABSTRACT: The application of machine learning to tasks involving volumetric biomedical imaging is constrained by the limited availability of annotated datasets of three-dimensional (3D) scans for model training. Here we report a deep-learning model pre-trained on 2D scans (for which annotated data are relatively abundant) that accurately predicts disease-risk factors from 3D medical-scan modalities. The model, which we named SLIViT (for 'slice integration by vision transformer'), preprocesses a given volumetric scan into 2D images, extracts their feature map and integrates it into a single prediction. We evaluated the model in eight different learning tasks, including classification and regression for six datasets involving four volumetric imaging modalities (computed tomography, magnetic resonance

SUBMITTER: Avram O 

PROVIDER: S-EPMC12695137 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature