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 imaging, optical coherence tomography and ultrasound). SLIViT consistently outperformed domain-specific state-of-the-art models and was typically as accurate as clinical specialists who had spent considerable time manually annotating the analysed scans. Automating diagnosis tasks involving volumetric scans may save valuable clinician hours, reduce data acquisition costs and duration, and help expedite medical research and clinical applications.

机器学习在涉及体积生物医学成像的任务中的应用受到用于模型训练的三维 （3D） 扫描注释数据集的可用性有限的限制。在这里，我们报告了一个在 2D 扫描上预先训练的深度学习模型（其注释数据相对丰富），该模型从 3D 医学扫描模式中准确预测疾病风险因素。我们命名为 SLIViT（意为“视觉转换器的切片集成”）的模型将给定的体积扫描预处理为 2D 图像，提取其特征图并将其集成到单个预测中。我们在 8 个不同的学习任务中评估了该模型，包括涉及四种体积成像模式 （计算机断层扫描、磁共振成像、光学相干断层扫描和超声） 的 6 个数据集的分类和回归。SLIViT 的性能始终优于特定领域的最先进模型，并且通常与花费大量时间手动注释分析扫描的临床专家一样准确。自动化涉及体积扫描的诊断任务可以节省临床医生宝贵的时间，降低数据采集成本和持续时间，并有助于加快医学研究和临床应用。