Work

Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training

Year: 2021

Type: conference-paper

Source: Lecture notes in computer science

DOI: 10.1007/978-3-030-78191-0_7

Authors: Yunhe Gao, Zhiqiang Tang, Mu Zhou, Dimitris Metaxas

Institutions: Rutgers, The State University of New Jersey, Shanghai Artificial Intelligence Laboratory, Centre for Perceptual and Interactive Intelligence

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Institutes of Health, Multidisciplinary University Research Initiative, National Heart, Lung, and Blood Institute

Awards: CHS: Medium: Data Driven Biomechanically Accurate Modeling of Human Gait on Unconstrained Terrain, AitF: Collaborative Research: Topological Algorithms for 3D/4D Cardiac Images: Understanding Complex and Dynamic Structures, S&AS: FND: COLLAB: Adaptable Vehicular Sensing and Control for Fleet-Oriented Systems in Smart Cities, Phase 1 IUCRC Rutgers-New Brunswick: Center for Accelerated Real Time Analytics (CARTA), Machine Learning and Deformable Model-based 4D Characterization of Cardiac Dyssynchrony from MRI, CHS: Medium: Collaborative Research: Scalable Integration of Data-Driven and Model-Based Methods for Large Vocabulary Sign Recognition and Search

OpenAlex ID: W3176226474

FWCI: 1.39

Cites: 31

Cited by: 22

Related to: 10