Head computed tomography (CT) imaging is a widely-used imaging modality with multitudes of medical indications, particularly in assessing pathology of the brain, skull, and cerebrovascular system. It is commonly the first-line imaging in neurologic emergencies given its rapidity of image acquisition, safety, cost, and ubiquity. Deep learning models may facilitate detection of a wide range of diseases. However, the scarcity of high-quality labels and annotations, particularly among less common conditions, significantly hinders the development of powerful models. To address this challenge, we introduce FM-CT: a Foundation Model for Head CT for generalizable disease detection, trained using self-supervised learning. Our approach pre-trains a deep learning model on a large, diverse dataset of 361,663 non-contrast 3D head CT scans without the need for manual annotations, enabling the model to learn robust, generalizable features. To investigate the potential of self-supervised learning in head CT, we employed both discrimination with self-distillation and masked image modeling, and we construct our model in 3D rather than at the slice level (2D) to exploit the structure of head CT scans more comprehensively and efficiently. The model's downstream classification performance is evaluated using internal and three external datasets, encompassing both in-distribution (ID) and out-of-distribution (OOD) data. Our results demonstrate that the self-supervised foundation model significantly improves performance on downstream diagnostic tasks compared to models trained from scratch and previous 3D CT foundation models on scarce annotated datasets. This work highlights the effectiveness of self-supervised learning in medical imaging and sets a new benchmark for head CT image analysis in 3D, enabling broader use of artificial intelligence for head CT-based diagnosis.

翻译：头部计算机断层扫描（CT）成像是一种应用广泛的影像学检查手段，尤其适用于评估脑、颅骨及脑血管系统的病理状况。凭借其扫描速度快、安全性高、成本低廉及普及性广等优势，CT常作为神经急症的首选影像学检查。深度学习模型有助于检测多种疾病，然而，高质量标签与标注的稀缺性（尤其是针对罕见病）严重制约了强大模型的开发。为此，我们提出FM-CT：一种面向头部CT的通用疾病检测基础模型，采用自监督学习进行训练。该方法在包含361,663份非增强三维头部CT扫描的大规模多样性数据集上预训练深度学习模型，无需人工标注，从而使模型学习到鲁棒且通用的特征。为探究自监督学习在头部CT中的潜力，我们同时采用了基于自蒸馏的判别式学习与掩码图像建模，并构建了三维（而非二维切片级）模型，以更全面高效地利用头部CT扫描的结构信息。模型的下游分类性能通过内部及三个外部数据集进行评估，涵盖分布内与分布外数据。结果表明，与从头训练的模型及以往三维CT基础模型相比，自监督基础模型在标注数据稀缺的下游诊断任务中性能显著提升。本工作突显了自监督学习在医学影像中的有效性，为三维头部CT图像分析设立了新基准，从而推动基于头部CT诊断的人工智能技术更广泛应用。