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
Query optimization is crucial for any data management system to achieve good performance. Recent advancements in AI have led academia and industry to investigate learning-based techniques in query optimization. In particular, many works propose replacing the cost model used during plan enumeration with a machine learning model that estimates the runtime of a plan. However, to build such a model …
Supervisors:
Zoi Kaoudi
Semester: Fall 2024
Tags: machine learning, training data, query optimizer
Optical fiber is the backbone of the internet’s communication, e.g. in the form of submarine fiber cables. It can also be employed as a sensor device, by means of combined opto-acoustic methods such as Distributed acoustic sensing (DAS) or State of Polarisation (SoP) sensing. Fiber is cabapble of sensing all kinds of vibrational/acoustic events, from animal sounds over seismic activity to …
Supervisors:
Sebastian Büttrich
Semester: Fall 2024
Tags: fiber, acoustics, audio, machine learning, DAS, SOP
The DISCO-2 project is driven by students and aims to develop and deploy a 3-unit CubeSat into low Earth orbit. Its mission focuses on conducting Earth observations over Greenland and supporting various research objectives. The satellite has three cameras onboard: infrared, wide-angle, and standard (main camera). Due to the limitations of the imaging hardware and the challenging conditions on the …
Supervisors:
Yucheng Lu, Julian Priest
Semester: Fall 2024
Tags: Image enhancement, Image processing, Machine learning
Deep convolutional networks are able to learn representation of images, scoring well in tasks such as image classification and object detection. During model training, these networks have the ability to process different input sizes without requiring changes to their architecture. In this project, we would like to investigate the effects that changing input sizes has on these kinds of models. We …
Supervisors:
Pınar Tözün, Ties Robroek
Semester: Fall 2024
Tags: data attribution, deep learning, machine learning, resource efficiency
Today’s foundation models are trained on vast amounts of data. The quality and size of this data has a huge impact on the accuracy of these models. Selecting the right amount and variety of data for a given task, however, is a resource-intensive process. In this project, we would like to investigate various state-of-the-art data selection mechanisms from a hardware requirements and …
Supervisors:
Pınar Tözün, Ties Robroek
Semester: Fall 2024
Tags: data selection, deep learning, machine learning, resource efficiency
It has been observed that deep learning models are able to identify patient characteristics such as age, sex, and self-reported race with high accuracy from medical images such as chest x-ray recordings, even when medical doctors cannot. This raises the potential for such models to learn to (falsely) diagnose patients of different demographics differently, even if they present with the same …
Supervisors:
Amelia Jiménez-Sánchez, Eike Petersen, Veronika Cheplygina
Semester: Fall 2024
Tags: machine learning, data science, medical imaging
Query optimization is crucial for any data management system to achieve good performance. Recent advancements in AI have led academia and industry to investigate learning-based techniques in query optimization. In particular, many works propose replacing the cost model used during plan enumeration with a machine learning model (typically a regression model) that estimates the runtime of a query …
Supervisors:
Zoi Kaoudi
Semester: Fall 2024
Tags: machine learning, database, query optimization, ranking