- Date: Wednesday, 3 August 2022
- Time: 12:30pm - 1:15pm
- Title: Learning with less labels for medical image segmentation
- Speaker: Dr. Mehrtash Harandi, Department of Electrical and Computer Systems Engineering, Monash University
- Register here
Abstract
Accurate segmentation of medical images is a key step in developing Computer-Aided Diagnosis (CAD) and automating various clinical tasks such as image-guided interventions.
The success of state-of-the-art methods for medical image segmentation is heavily reliant upon the availability of a sizable amount of labelled data. If the required quantity of labelled data for learning cannot be reached, the technology turns out to be fragile.
The principle of consensus tells us that as humans, when we are uncertain how to act in a situation, we tend to look to others to determine how to respond. In this webinar, Dr Mehrtash Harandi will show how to model the principle of consensus to learn to segment medical data with limited labelled data. In doing so, we design multiple segmentation models that collaborate with each other to learn from labelled and unlabelled data collectively.
Biography
Dr Mehrtash Harandi is a senior lecturer in the Department of Electrical and Computer Systems Engineering (ECSE) at Monash University and a contributing research scientist at the Machine Learning Research Group (MLRG - Data61-CSIRO). His research spans various aspects of machine learning, computer vision and signal processing, developing algorithms and mathematical models to equip machines with intelligence.
Enquiries
T: +61 3 9905 0100 | E: enquiries.mbi@monash.edu
