Machine Learning for Biological and Medical Imaging

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Project Description

We have recently developed hybrid machine learning techniques for retinal images. For publications see google scholar(https://scholar.google.com/citations?hl=sv&user=_DUppAgAAAAJ&view_op=list_works&sortby=pubdate). Challenges include limited number of images, unbalanced data-sets, and interpretability of feature representations. Subprojects include to

Formulation and training of robust generative models (e.g.GANsand versions thereof) for the Retinal Dataset-

Extend and apply the techniques to melanoma datasets Develop and apply techniques to identify meaningful (biological/medical) feature representation from a successfulclassification

Program - Computer Science

Division - Computer, Electrical and Mathematical Sciences and Engineering

Faculty Lab Link - https://livingsystems.kaust.edu.sa/

Field of Study - Computer Science, Applied Mathematics

About the
Researcher

Jesper Tegner

jesper.tegner@kaust.edu.sa

Desired Project Deliverables

Individual projects will be tailored and narrowly designed from the above palette according to interest of the student, technical proficiency, and level of study. We expect you(a)tobringenthusiasm,creativity,andhardwork,(b)givelabseminarsonyourwork, and (c) produce a final written report.In returnthis facilitates your critical thinking, presentations skills, and scientific writing.Yourresearch, in collaboration and with support of team members, may lead to scientific publications. You will also get a good hands-on perspective at the frontier of machine intelligence and its applications in an interdisciplinary research group andenvironment.