MSc Projects

We no longer accept proposals for Master internships. An overview of past projects can be found below.

Project Vacancies

Predicting successful statin medication treatment

Development of a prediction tool for successful statin medication treatment

Automated whole-slide image analysis and quality control

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

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

2D Plane Detection in 3D prenatal ultrasound

Detection and analysis of 2D planes of interest in 3D ultrasound images

Automated Detection and Grading of Hip Osteoartritis

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

Body composition assessment in 3D CT and MR images

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

Automated analysis of intracoronary OCT images for patients with acute myocardial infarction

Development of a model for the automatic analysis of intracoronary optical coherence tomography (OCT) images obtained during cardiac catheterization in patients with acute myocardial infarction

Interventional reconstruction AI for real-time needle tracking in MRI

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

Few-shot learning for medical image segmentation

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

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.

Quantification of immunohistochemical markers for improved prostate cancer prognostics

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

Robust identification of the L3 vertebra

Develop a method to label segmented vertebra on CT scans that is robust to abnormalities and anatomical variants.

Spying on parasites: using deep learning to quantify the interactions between malaria parasites and human liver cells

Quantifying interactions between malaria parasites and human liver cells

Deep behavioral phenotyping of mosquito biting behavior

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

Identification of features in benign breast disease biopsies that predict breast cancer risk

Development of a deep learning system to predict BC risk in H&E

Self-supervised pretraining for digital pathology

Development of self-supervised pretraining for digital pathology.

Automated detection of progression of white matter hyperintensities in cerebral small vessel disease using machine learning

Development of a segmentation model for WMH in in vivo and post-mortem brain MRI and detection of WMH progression.

Three dimensional facial landmark detection in 3D photos

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

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

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.

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.

Automated AAA detection

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

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

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

Artificial intelligence-assisted detection of adhesions on cine MRI

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

Commercial AI marketplaces for radiology

Investigating the surge of commercial AI marketplaces for radiology

AI steered interventional MRI

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

AI-based quantification of non-alcoholic steatohepatitis

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

Artifact detection in digitized histopathology images

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

Bradykinesia assessment in Parkinson’s disease

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

Improving detection of COVID-19 classification with CT scans

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

Automated COVID-19 classification using ultrasound

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

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.

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.

Automated landmark detection on lateral headplates for orthodontic diagnosis

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

Facial phenotyping of intellectual disability patients

Development of a deep learning algorithm for learning face representations.

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.

Fetal heart rate detection in twin pregnancies

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

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.

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.

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.

Detecting and quantifying lymphocytes in CD8, CD3 and Ki-67 marked immunohistochemistry slides using deep learning.

Project aimed at development of deep learning algorithms for the identification of lymphocytes in IHC staining.

Color deconvolution for color-agnostic and cross-modality analysis of immunohistochemistry whole-slide images with deep learning

Applying models trained on immunohistochemistry data to differently-stained and multiplex immunofluorescence data

MSc Projects

Project Vacancies

Running Projects

Completed Projects