Applying Generative Adversarial Network in Medical Image Analysis

Project Member(s): Li, J.

Funding or Partner Organisation: Southern University of Science and Technology
Southern University of Science and Technology

Start year: 2020

Summary: The project is focused on developing generative adversarial network (GAN)-based image analytic techniques and related theories for common tasks such as detection, classification and sgementation of medical images.

Publications:

Shaham, S, Ding, M, Liu, B, Lin, Z & Li, J 1970, 'Machine Learning Aided Anonymization of Spatiotemporal Trajectory Datasets', IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, pp. 1-6.
View/Download from: Publisher's site

FOR Codes: Image processing, Knowledge Representation and Machine Learning, Information and Communication Services not elsewhere classified, Information Processing Services (incl. Data Entry and Capture), Machine learning, Information systems, technologies and services not elsewhere classified, Other information and communication services not elsewhere classified

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.