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

Reproducibility is a cornerstone of the scientific method. There are systems available today to build reproducible and sharable data and analysis pipelines including workflow engines (e.g., GWL, Nextflow), package managers (e.g., bioconda), and container systems (e.g., Singularity). However, validating their executions on high-performance computers remains an open issue. Indeed, there are many …
Supervisors: Philippe Bonnet
Semester: Fall 2020
Tags: ML, reproducibility, workflow, HPC

Reproducibility is a cornerstone of the scientific method. It is also a core element of compliance requirements for sensitive equipment, e.g., audit trails for medical equipment. Often, a prerequisite for computational reproducibility is the availability of software and data. However, this is problematic for edge devices whose goal is to reduce the amount of data transferred to the backend. On …
Supervisors: Philippe Bonnet
Semester: Fall 2020
Tags: reproducibility, edge