Publications by authors named "Tejasv Bedi"
Article Synopsis
- Medical imaging, especially digital pathology, is transforming cancer diagnosis and treatment by providing detailed cellular-level images.
- Recent advancements in deep learning have improved the segmentation of tumor regions, highlighting the need for new statistical models to describe tumor shapes instead of relying on outdated traditional methods.
- This paper introduces a Bayesian model that analyzes tumor boundaries as polygonal chains, effectively estimating landmark uncertainty and quantifying boundary roughness, which was found to significantly predict patient prognosis in lung cancer cases.
Background: Forecasting of COVID-19 cases daily and weekly has been one of the challenges posed to governments and the health sector globally. To facilitate informed public health decisions, the concerned parties rely on short-term daily projections generated via predictive modeling. We calibrate stochastic variants of growth models and the standard susceptible-infectious-removed model into 1 Bayesian framework to evaluate and compare their short-term forecasts.
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