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Automated Analysis of Multi-modal Medical Data using Deep Belief Networks

Recently, magnetic resonance and ultrasound imaging have found utility as adjuncts to mammography in the detection and management of breast cancer. This project will develop novel machine learning techniques that optimally integrate information from each of these data sources so as to improve the efficiency and accuracy of breast cancer diagnosis.

Centre of Excellence  for Robotic Vision

The centre’s research will allow robots to see, to understand their environment using the sense of vision. This is the missing capability that currently prevents robots from performing useful tasks in the complex, unstructured and dynamically changing environments in which we live and work.

Precision Radiology

Using Deep Learning, we are developing deep learning models that aim to identify the general health condition of a patient, using mortality as a training annotation.

Automatic Quantification of Acute and Chronic Hypoxia in Tumors from
Immunohistochemical Fluorescence Images using Deep Structured Inference

Segmentation and tracking of the human heart in 2D and 3D ultrasound data based on a principled combination of the top-down and bottom-up paradigms

Automatic 3-D Modelling from Complex Artistic Prints

In this project, we aim to take a few images of your favourite cartoon, and build a 3D mesh with a texture atlas.

 PrintArt - Image Retrieval and Annotation for Digitized Art.

In this project, we have two aims: 1) how to assemble a 2D puzzle that form a large artistic tile panel, and 2) formulate meaningful artistic image representations for classification and retrieval problems.

Learning to Combine Hierarchical Image Modeling with 2-D Segmentation and 3-D Pose Recovery of Visual Objects

Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree

Precise detection and delineation of several fetal anatomies from ultrasound using probabilistic boosting tree.

Supervised Learning of Semantic Classes for Image Annotation and Retrieval

In this work, we propose the use of a hierarchical extension of expectation-maximization for the supervised learning of semantic visual classes.

Image Representation using Local Image Features 

This is older work on the design of representation methods using local image features.