Bogdan Georgescu

TL;DR: In this article, a curriculum learning based loss function is proposed to handle the imbalance problem in both the input and output of a learning model for multi-organ segmentation, where the Dice similarity coefficient is leveraged to deter model parameters from being held at bad local minima and at the same time gradually learn better model parameters by penalizing for false positives/negatives using a cross entropy term.

TL;DR: A new curriculum learning based loss function that leverages Dice similarity coefficient to deter model parameters from being held at bad local minima and at the same time gradually learn better model parameters by penalizing for false positives/negatives using a cross entropy term is introduced.

TL;DR: A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores, based on deep learning and deep reinforcement learning.

TL;DR: In this paper, the authors describe recently developed technologies for better handling of image information: photorealistic visualization of medical images with Cinematic Rendering, artificial agents for in-depth image understanding, support for minimally invasive procedures, and patient-specific computational models with enhanced predictive power.

TL;DR: In this paper, a system and method for tracking a global shape of an object in motion is disclosed, where one or more control points along an initial contour of the global shape are defined.