Yefeng Zheng

TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.

TL;DR: An automatic four-chamber heart segmentation system for the quantitative functional analysis of the heart from cardiac computed tomography (CT) volumes is proposed and an efficient and robust approach for automatic heart chamber segmentation in 3D CT volumes is developed.

TL;DR: In this paper, an end-to-end 3D convolutional neural network (CNN) composed of mutually beneficial generators and segmentors was proposed for image synthesis and segmentation tasks.

TL;DR: This paper introduces the notion of a neighborhood structure for the general point matching problem, and forms point matching as an optimization problem to preserve local neighborhood structures during matching.

TL;DR: This work couple the modeling of the anatomy appearance and the object search in a unified behavioral framework, using the capabilities of deep reinforcement learning and multi-scale image analysis, and significantly outperforms state-of-the-art solutions on detecting several anatomical structures with no failed cases from a clinical acceptance perspective.