Multi-task semantic segmentation on medical images

External Member

David Ahmedt-Aristizabal


Abstract: This project aims to develop a learning system that can solve different semantic segmentation task in the area of healthcare. This can be achieved through the use of a single learner, an ensemble of multiple learners, architecture search, or any other technique, as long as task-specific model parameters are not human-defined (i.e. any parameter tuning has to happen automatically and algorithmically). Dataset: public datasets (e.g. brain tumour, heart, liver, hippocampus, prostate, lung, pancreas, hepatic vessels, spleen and colon).

Contact: David Ahmedt-Aristizaba

Updated:  1 June 2019/Responsible Officer:  Dean, CECS/Page Contact:  CECS Marketing