5 citations · 8 across the 7 of their papers we have counts for
5 papers · 1 filter
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Jun Ma, Feifei Li, Sumin Kim +79
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive co…
Advancing Stroke Risk Prediction Using a Multi-modal Foundation Model
Camille Delgrange, Olga Demler, Samia Mora +3
Predicting stroke risk is a complex challenge that can be enhanced by integrating diverse clinically available data modalities. This study introduces a self-supervised multimodal f…
ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset
Evamaria Olga Riedel, Ezequiel de la Rosa, The Anh Baran +18
Stroke remains a leading cause of global morbidity and mortality, imposing a heavy socioeconomic burden. Advances in endovascular reperfusion therapy and CT and MR imaging for trea…
ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?
Ezequiel de la Rosa, Ruisheng Su, Mauricio Reyes +37
Accurate estimation of brain infarction (i.e., irreversibly damaged tissue) is critical for guiding treatment decisions in acute ischemic stroke. Reliable infarct prediction inform…
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew +55
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable A…