A personalized Uncertainty Quantification framework for patient survival models: estimating individual uncertainty of patients with metastatic brain tumors in the absence of ground truth

no code implementations • 28 Nov 2023 • Yuqi Wang, Aarzu Gupta, David Carpenter, Trey Mullikin, Zachary J. Reitman, Scott Floyd, John Kirkpatrick, Joseph K. Salama, Paul W. Sperduto, Jian-Guo Liu, Mustafa R. Bashir, Kyle J. Lafata

We evaluated our method on multiple clinically-relevant endpoints, including time to intracranial progression (ICP), progression-free survival (PFS) after SRS, overall survival (OS), and time to ICP and/or death (ICPD), on a variety of both statistical and non-statistical models, including CoxPH, conditional survival forest (CSF), and neural multi-task linear regression (NMTLR).

Time-to-Event Prediction Uncertainty Quantification

Duke Spleen Data Set: A Publicly Available Spleen MRI and CT dataset for Training Segmentation

no code implementations • 9 May 2023 • Yuqi Wang, Jacob A. Macdonald, Katelyn R. Morgan, Danielle Hom, Sarah Cubberley, Kassi Sollace, Nicole Casasanto, Islam H. Zaki, Kyle J. Lafata, Mustafa R. Bashir

Spleen volumetry is primarily associated with patients suffering from chronic liver disease and portal hypertension, as they often have spleens with abnormal shapes and sizes.

Segmentation

Deep learning in radiology: an overview of the concepts and a survey of the state of the art

no code implementations • 10 Feb 2018 • Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha, Mustafa R. Bashir

In this article, we review the clinical reality of radiology and discuss the opportunities for application of deep learning algorithms.