MSc Projects

We are continuously looking for interesting new projects. More information about project proposals can be found here.

MSc students are eligible to receive a monthly reimbursement of €500,- for a period of six months. For more information, please read the requirements. An overview of current project vacancies can be seen below.

Project Vacancies

Identification of drug repurposing candidates for myotonic dystrophy by Graph Convolutional Networks

Develop a method to identify of drug repurposing candidates for myotonic dystrophy by Graph Convolutional Networks

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Natural language processing of radiology reports for lesion detection

Develop a method to automatically find statements in radiology reports on presence, size and type of lesions in CT scans.

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Predicting treatment for Addictive Behaviors in Clinical practice (PreT-ABC)

Development of a method for the identification of patient-related predictors of treatment outcome.

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Automated landmark detection on lateral headplates for orthodontic diagnosis

Development of a method for automatic facial landmark detection in cephalograms.

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Machine Learning in Acute Care: Liver

Will deep learning-based algorithms become the new members of the trauma team?

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Machine Learning in Acute Care: Spleen

Will deep learning-based algorithms become the new members of the trauma team?

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Running Projects

Automated AAA detection

Project aimed at development of deep learning algorithms for automated detection of AAA.

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Artificial intelligence-assisted detection of adhesions on cine MRI

Development of an AI-assisted algorithm for automatic detection of adhesions on cine MRI

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Improving detection of COVID-19 classification with CT scans

Development of deep learning algorithms and a web application for automated classification of COVID-19.

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Detecting Fractures in the Radius, Ulna, and Metacarpal Bones on Conventional Radiographs

Development of a deep learning algorithm and web application for automated detection of fractures in the radius, ulna, and metacarpal bones on conventional radiographs.

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Exploring Multi-task Learning for Improving Diagnosis in General Practice

Development of a model to determine probable diagnoses for common reasons to visit a General Practitioner.

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Diagnosis Prediction in General Practice: The Importance of Temporality in EPRs

Development of a model to determine probable diagnoses for common reasons to visit a General Practitioner.

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Three dimensional oral and maxillofacial surgical outcome prediction

Development of a model for accurate facial profile outcome prediction following oral and maxillofacial surgery.

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Automated Quantification of Tumor-Infiltrating Lymphocytes

Developing an algorithmn that can automatically detect and segment tumor-infiltrating lymphocytes in breast cancer.

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Detecting and characterizing vertebral fractures in CT scans

Developing image analysis algorithms that automatically detect osteoporotic vertebral fractures.

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Completed Projects

AI steered interventional MRI

Develop Artificial Intelligence (AI) to track tumor targets in interventional MRI.

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Artifact detection in digitized histopathology images

Development of a deep learning algorithm that can classify the different types of artifacts in whole slide images.

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Bradykinesia assessment in Parkinson’s disease

Development of a model for the automatic identification of Parkinson's disease based on a keyboard test.

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Automated COVID-19 classification using ultrasound

Development of deep learning algorithms and web application for automated classification of COVID-19.

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Facial phenotyping of intellectual disability patients

Development of a deep learning algorithm for learning face representations.

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Identify fever etiology in ICU patients with acute brain injury

Development of a model that can identify whether a febrile ICU patient with acute brain injury has an infectious fever or non-infectious fever.

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Predicting Clinical Deterioration Events

Predicting clinical deterioration events in hospitalized patients by using novel machine learning techniques.

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Segmenting CT images for body composition assessment

We develop algorithms for segmentation of muscles and fat tissue in 3D CT images.

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Automatic screening for neuromuscular disorders

Development of a deep learning algorithm for the automatic classification of muscle ultrasound images.

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Modelling long-term progression of Parkinson’s

Development of a model to support treatment decisions regarding cardiovascular risk management in patients with Parkinson’s disease (PD).

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Pneumothorax detection

Development of a system to detect pneumothorax in frontal chest radiographs.

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Detection of tumor and immune cells in PD-L1 stained histopathology lung cancer whole-slide images

Project aimed at development of deep learning algorithms for the (semi-) automated scoring of PD-L1 positive tumor cells, an established biomarker for immunotherapy treatment response in lung cancer patients.

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Predicting changes in quality of life of ICU survivors

Development of a model for prediction of quality of life.

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Automated prenatal ultrasound screening

Project aimed at development of deep learning algorithms for automated detection of twin pregnancies and placenta localization.

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Programmatically Generating Annotations for De-identification of Clinical Data

Development of machine learning systems to find and annotate protected health information in medical records.

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Automated clinical scoring in psoriasis

Development of automatic classification algorithm for psoriasis in photographs of the body.

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Simulated Prosthetic Hearing in deaf subjects

Development of a neural network based model that improves speech perception in cochlear implant recipients, by optimizing the vocoder strategy in order to restore binaural hearing in deaf subjects.

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Text mining pathology reports

Development of a text mining system to accurately make a diagnosis from nephrology pathology reports.

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