Prostate cancer is one of the most common cancer types among men worldwide. Magnetic Resonance
Imaging (MRI) plays an important role in the detection of prostate cancer. Radiologists can identify
lesions on the prostate MRI and assign a risk score to each lesion. Men with a high risk for prostate
cancer receive a prostate biopsy. In clinical practice, more than 40% of the prostate biopsies are
negative i.e., no cancer cells were found. We aim to reduce the number of negative biopsies.
Deep learning models can be used to objectively predict prostate cancer from MRI scans. The goal of
this thesis is to develop, train and evaluate such deep learning models.

The aim of this thesis is to use state-of-the-art image embedders and/or train an image encoder that
extracts features from prostate MRI scans. These features can be used for downstream tasks such as
the prediction of prostate cancer.
The dataset of the PI-CAI (Prostate Imaging: Cancer AI) challenge will be used to train and test the
deep learning models.
The objectives of the master thesis are:
1. Reviewing state-of-the-art 3D image embedders in medical imaging.
2. Extracting features from prostate MRI’s.
3. Predicting clinically significant prostate cancer with the extracted features.
4. Comparing the performance of different model architectures and configurations.

The project consists of following tasks:
- Literature study
- Downloading and processing of the PI-CAI dataset
- Implementation and training of 3D image encoder
- Classification of extracted features
- Evaluation and comparison of image encoder and classification models

(Mandatory) qualifications:
- Following an MSc in a field related to one or more of the following: Computer Science,
Biomedical Engineering, Applied Computer Science - Digital Health.
- Strong programming skills (Python).
- Ability to write scientific reports and communicate research results in English.