3D foundation model for generalizable disease detection in head computed tomography.
Authors
Affiliations (10)
Affiliations (10)
- Center for Data Science, New York University, New York, NY, USA.
- Center for Data Science, New York University, New York, NY, USA. [email protected].
- Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA.
- Siemens Healthineers, Malvern, PA, USA.
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA.
- Department of Neuroscience and Physiology, NYU Grossman School of Medicine, New York, NY, USA.
- Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, USA.
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA. [email protected].
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA. [email protected].
Abstract
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 used as 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, substantially hinders the development of powerful models. To address this challenge, we introduce FM-HCT, a Foundation Model for Head CT for generalizable disease detection, trained using self-supervised learning. Our approach pretrains 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. Our results demonstrate that the self-supervised foundation model substantially improves performance on downstream diagnostic tasks compared to models trained from scratch and previous 3D CT foundation models trained on scarce annotated datasets.