Clinical or translational research on adult cancer cases in U-CAN
Opportunity for students to work with clinical data or sample analyses based on the cancer cases included in U-CAN.
The aim is to test a scientific hypothesis in clinical or translational cancer research by analysis of samples or data in a database of cancer patients in U-CAN.
Machine learning and computer vision is being used more and more extensively for solving various radiology tasks. Nevertheless, clinical radiology is still heavily dependent on manual inspection of images. Automation of steps in the image review process can contribute to e.g. more efficient prioritization of cases for the radiologist’s review, and shorter time to diagnosis for the patient. There are however great demands on such tools to be able to use them in the clinical setting.
In this project, we are developing machine learning models and pipelines for automatic detection or grading of common conditions, such as knee osteoarthritis and pulmonary embolism. For the work, several large data sets of X-ray and CT images from e.g. lungs and knees have been annotated by our collaborating radiologists.
We are looking for students to participate in model development and building of different automated pipelines for clinical radiology tasks. Knowledge of the basics of machine learning and some basic programming skills are required. Experience of using Python for data science and/or machine learning is an advantage.
Tobias Sjöblom, Professor
Read more about our research here.