To optimise the implementation of DL models in the clinical radiotherapy workflow, we are developing an automated quality assurance (auto-QA) system that can automatically detect when the model makes a mistake and what the consequences of these mistakes are for the patient’s treatment plan. One of the elements of this system will be to detect when a DL segmentation model is applied to data outside its training distribution.
- You will conduct an overview of recent literature on promising out-of-distribution detection methods
- You will implement, analyse, and compare different methodologies to detect out-of-distribution inputs to a DL segmentation model (e.g. generative AI, uncertainty quantification, model feature distance, etc.)
- You will collaborate with the research team on how to integrate this element in the greater auto-QA tool
- You will contribute to the usability of AI by improving the implementation of AI in a clinical workflow
- There is room for your own input
- There is interest to turn the results into an academic publication
For this project, we are looking for a university master’s or bachelor’s student with, for example, a background in artificial intelligence, computing science, applied mathematics, or a similar field.
- You have an interest in clinically applicable research
- You have experience with ML/DL modelling
- You are able to independently conduct research and have strong writing skills
- You enjoy collaborating with team members to develop innovative solutions
