We investigate how token granularity and model size affect EEG-language model performance in both in-distribution and cross-site scenarios, and find that token granularity is a critical, task-dependent scaling dimension for clinical EEG models, sometimes more important than model size.
We present NYUMets-Brain, the world’s largest, longitudinal, real-world dataset of cancer consisting of the imaging, clinical follow-up, and medical management of 1,429 patients. Using this dataset we developed Segmentation-Through-Time, a deep neural network which explicitly utilizes the longitudinal structure of the data and obtained state-of-the-art results at small (<10 mm3) metastases detection and segmentation.
We trained a large language model for medical language (NYUTron) and subsequently fine-tuned it across a wide range of clinical and operational predictive tasks, and found that it outperforms traditional models while being much easier to deploy.
We present a graph neural network that is able to track cortical spreading depressions in scalp EEG signals. We show that our model is scalable to different densities of EEG and generalizable to different head models.