A Deep Learning Approach to Fusion of Multimodal Biomedical Data

21 Dec 2023

Supervisor: Dr. Ben Mashford (CECC & JCSMR)

Description: Machine learning offers promising potential for advancing our understanding of disease mechanisms through analysis of diverse biomedical data. Each day, hospitals and research institutes generate clinical patient data from a range of instruments and the effective analysis of these datasets is a challenging technical task.

A key goal in this field of research is the development of models that link phenomic data across disparate measurement types, such as: medical imaging, genomic sequencing and blood antibody levels. Deep learning (DL) networks have proven to be powerful analysis tools when applied to high-dimensional & noisy clinical data and ongoing work is focused on developing new multimodal DL models for the combined analysis of complex, heterogeneous datasets.

This project will be conducted alongside researchers at ANU’s John Curtin School of Medical Research who are applying deep learning methods to understanding the causes and mechanisms of autoimmune disease.

Requirements: This project requires a interest and/or background in deep learning, including Pytorch and related Python modules. An interest in medical data and applications to healthcare are highly desirable.

Gain: Experience with applying machine learning to real scientific data.
Develop an understanding of the use of data analytics in the biomedical domain.

Contact: Dr. Ben Mashford (benjamin.mashford@anu.edu.au)

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