Reageer t/m 12 mei
Are you interested in improving the implementation of AI and clinical practice? Do you want to contribute to research that develops user-friendly tools to optimise radiotherapy workflows and increase the adoption of DL models?
To optimise the implementation of deep learning (DL) models in the clinical radiotherapy workflow, we are developing an automated quality assurance (auto-QA) system that can detect when and where a DL model makes mistakes. A critical aspect of this system is ensuring that clinicians can easily and effectively use it in their daily practice and that their needs are taken into consideration.
At the department of radiotherapy, a radiation treatment plan is made for every individual patient based on imaging scans. The tumour and organs at risk are delineated to optimise dose to the target and minimise dose to healthy surrounding tissue. However, manual delineation is time-consuming, so we have implemented automated delineation with a deep learning (DL) model since 2018. While DL models have high average accuracy, they can make mistakes on individual patients or structures. Therefore, all DL segmentations must be evaluated by clinicians. This requirement reduces clinician trust, limits usability and limits the adoption of DL models.
Therefore, there is a growing interest in methods to automatically assess the quality of DL segmentation and predict the influence of errors on the treatment plan. This information can then be provided alongside the model output to improve clinical implementation of the DL model. Recent research within the UMCG and other hospitals has developed technical solutions for error detection in DL segmentation, but the next challenge is designing tools that clinicians will actually want to use and that present this information in an intuitive way. This requires understanding their workflows, preferences and pain points with DL segmentation.
Therefore, we are looking for a master’s thesis student who wants to contribute to developing a clinician-centred auto-QA tool that can be integrated into the radiotherapy workflow to increase the adoption of DL models.
For this project, we are looking for a university master’s or bachelor’s student with a background in human-machine interaction, computational cognitive sciences, behavioural sciences, neuroscience, or a similar field.
Good to know: in consultation, you can partly work from home.
Interested?
Feel free to take some time to consider this vacancy, but don’t wait too long… We will close the vacancy once we find a suitable candidate (the closing date is fictitious).
You can easily apply via the application button.
After receiving your application, you will immediately receive a confirmation. We select once a week and invite suitable candidates for an interview. Is there a match? Then we will register you for the UMCG internship agreement.
We vinden het leuk als je contact met ons opneemt als je vragen hebt over het werken bij het UMCG.
