Machine Learning for Biological and Medical Imaging

Machine Learning for Biological and Medical Imaging

Internship Description

We have recently developed hybrid machine learning techniques for retinal images. For publications see google scholar ( rtby=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. GANs and versions thereof) for the Retinal Dataset

-          Extend and apply the techniques to melanoma data-sets

Develop and apply techniques to identify meaningful (biological/medical) feature representation from a successful classification


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) to bring enthusiasm, creativity, and hard work, (b) give lab seminars on your work, and (c) produce a final written report. In return this facilitates your critical thinking, presentations skills, and scientific writing. Your research, 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 and environment​.

Faculty Name

Jesper Tegner

Field of Study

Computer Science, Applied Mathematics