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

AI-Designed 3D prothesis for 3D Printing in third world countries

Development of a deep learning algorithm for automatically designing prosthetics based on 3D scans

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AI-based quantification of non-alcoholic steatohepatitis

Develop a method to quantify non-alcoholic steatohepatitis in histopathology images

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Developing deep learning algorithm for de novo variants detection in Pacbio long-read sequencing data

Developing deep learning algorithm for de novo variants detection in Pacbio long-read sequencing data.

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Interventional reconstruction AI for real-time needle tracking in MRI

Development of an interventional reconstruction algorithm for real-time needle tracking in MRI

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Deep behavioral phenotyping of mosquito biting behavior

Development of a deep learning algorithm for phenotyping of mosquito biting behavior in video

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Automated whole-slide image analysis and quality control

Development of a deep learning algorithm for automated whole-slide pathology image analysis and quality control

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Self-supervised pretraining for digital pathology

Development of self-supervised pretraining for digital pathology

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Running Projects

Natural language processing of radiology reports for lesion detection

Develop a method to automatically find statements in radiology reports on the 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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Commercial AI marketplaces for radiology

Investigating the surge of commercial AI marketplaces for radiology

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Automated Detection and Grading of Hip Osteoartritis

We want to develop deep learning algorithms for detection and grading of hip osteoartritis.

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Body composition assessment in 3D CT and MR images

Automatic quantification of muscle and fat tissue in 3D CT and MR images

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Few-shot learning for medical image segmentation

Develop a 3D segmentation method that can learn a task from only a few segmented 2D slices.

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Automated Detection of Developmental Hip Dysplasia

Develop a deep learning algorithms for automated detection developmental hip dysplasia. The algorithms should run on a phone with a low-cost portable ultrasound probe attached.

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Quantification of immunohistochemical markers for improved prostate cancer prognostics

Development of a model for the quantification of immunohistochemical markers for improved prostate cancer prognostics

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Generic out-of-distribution detection for radiology AI systems

Develop a method for out-of-distribution detection to make AI more reliable and robust.

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Deep placental vessel segmentation and registration for fetal laser surgery

Development of a model for deep placental vessel segmentation and registration for fetal laser surgery

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Domain Generalization for Prostate Cancer Detection in MRI

Develop a method for domain generalization for prostate cancer detection in MRI

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Extending a prostate cancer grading algorithm to other surgical entities

Develop a method to extend a prostate cancer grading algorithm to other surgical entities

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Scoliosis simulation for improving a segmentation and labelling algorithm

Modeling of deformities in adolescent idiopathic scoliosis to improve segmentation

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Automatic classification and segmentation of subsolid pulmonary nodules using deep learning

Development of deep learning algorithms for subsolid nodule analysis in CT.

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WML in ex vivo brain MRI

Development of a segmentation model for WML in ex vivo brain MRI

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Completed Projects

Three dimensional facial landmark detection in 3D photos

Development of a model for the automatic detection of clinically relevant facial landmarks in 3D photos.

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Drug repurposing with graph neural networks for COVID-19 and Myotonic Dystrophy type 1

Develop a method to identify drug repurposing candidates for myotonic dystrophy and COVID-19 by Graph Convolutional Networks

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Automated AAA detection on CT scans

We want to develop a robust deep learning algorithm for automated detection of abdominal aorta aneurysms in CT scans.

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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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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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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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Automated COVID-19 classification using ultrasound

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

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AI-assisted detection of endometrium (pre)malignancies in endometrium pipelle biopsies

The development of model to detect (pre)malignancies in highly fragmented pipelle sampled biopsies.

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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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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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Fetal heart rate detection in twin pregnancies

Development of a model to determine the individual fetal heart rates in twin pregnancies.

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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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Machine Learning with Electronic Patient Records for Diagnosis Prediction in General Practice

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

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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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Applications of deep learning on orthostatic hypotension detection

Develop a method to predict orthostatic hypotension in realtime for early diagnosis

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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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Three dimensional oral and maxillofacial surgical outcome prediction

Development of a deep learning method that can generate 3D facial profiles of a patient after orthognathic surgery provided the planned surgical parameters.

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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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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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Machine Learning in Acute Care: Liver & Spleen

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

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3D Convolutional Network based cancer vaccine candidate predictions

Develop an AI method to identify cancer vaccine candidates using 3D Convolutional Networks - a proof of concept

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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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