Machine learning methods are often evaluated on benchmark datasets, in computer vision, medical imaging, NLP and other fields. In such evaluation, researchers often describe the data as being: representative, for example based on the distribution of ages of the patients mirroring the world population, similar, for example because both dataset contain pictures of animals diverse, for example …
Supervisors: Veronika Cheplygina
Semester: Spring 2025
Tags: machine learning, medical imaging, data analysis, meta-research

In medical imaging, multi-task learning can be used to train a model that jointly predicts both a diagnosis, and other patient characteristics, such as demographic variables. Among others, this strategy has frequently been used for diagnosis of Alzheimer’s from brain MR scans, with age as an additional variable, see Zhang et al as an example. The idea is that both the disease, and age, …
Supervisors: Veronika Cheplygina
Semester: Spring 2025
Tags: machine learning, medical imaging, data analysis, fairness

Machine learning methods for medical imaging, for example segmentation of skin lesions or classification of lung cancer, are often evaluated on benchmark datasets such as ISIC, CheXpert, MIMIC-CXR and so forth. In such evaluation, researchers often compare the methods they propose, to state-of-the-art methods in the field, and report various performance metrics such as Dice score, AUC etc. Due to …
Supervisors: Veronika Cheplygina
Semester: Spring 2025
Tags: machine learning, medical imaging, data analysis, meta-research, reproducibility

There is pressure on hospitals to implement AI systems which promise to improve diagnoses and save time for the doctors. One use-case could be related to the automation of protocoling based on a physician referral. Currently, this requires a referral letter from a physician who has examined a patient and evaluates that there is a need for additional imaging studies. In this case, the physician …
Supervisors: Veronika Cheplygina
Semester: Fall 2023
Tags: machine learning, medical imaging, data analysis

Machine learning models, especially larger models that are used in for example image or text datasets, can be expensive to train. During development models are usually trained multiple times for example to optimize hyperparameters, which can result in a large carbon footprint. This project specifically focuses specifically on medical data. There are some recent efforts, for example by Selvan et …
Supervisors: Veronika Cheplygina
Semester: Fall 2023
Tags: machine learning, medical imaging, data analysis, resource consumption

Outlier detection is carried out when the information is stored at the server. However, with the new IoT computational capabilities, outlier detection can be developed locally. Therefore, it is necessary to know how much RAM/Flash is needed for this step and which IoT brands can handle it. This project is divided into two parts. The first is implementing light-heavy ML algorithms in single points …
Supervisor: Paul Rosero
Semester: Spring 2022
Tags: data analysis, IoT, Python, Embedded systems

TinyML is a new trend to deploy deep learning in tiny devices. Therefore, it is necessary to deploy several applications to understand the challenges and opportunities which tinyML brings us. In this scenario, any idea with embedded computer vision, voice recognition, and sensors are welcome.
Supervisor: Paul Rosero
Semester: Spring 2022
Tags: data analysis, IoT, Python, Embedded systems, Computer vision, Voice recognition

Energinet has a model that describes the electricity production of a given wind turbine given wind conditions. The current model based on kNN is trained with DMI weather data and historical electricity production data for the wind turbine. The goal of the project is to improve the current model with lifelong learning, extended weather data and different models for a range of different wind …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Data Analysis