Sperm-net for ML research
Contribute to the future of AI-driven sperm analysis by curating, organizing, and testing open-access datasets.
The primary objective of this project is to create a well-organized sperm image and video dataset from various open-access studies, tailored for machine learning research. This involves identifying and collecting relevant open-access datasets, analyzing and preparing them for use, and organizing them into a comprehensive repository on Hugging Face. The repository will include carefully structured train and test splits to facilitate reproducibility. Additionally, the project will implement one or two case studies, such as predicting sperm motility or morphology, to evaluate the dataset's effectiveness and demonstrate its potential for training foundation models.
Goal
The primary goal of this project is to compile a comprehensive sperm image and video dataset from various open-access studies, organizing it for high-quality and reproducible machine learning research.
Learning outcome
- Sperm video analysis using AI/ML
- Machine learning and artificial intelligence
- Building and structuring databases for machine learning research
- Writing a scientific paper and how to publish it
Qualifications
- Hardworking and motivated
- Proficient in Python programming
- Eager to learn
- (Additional skills can be acquired during the thesis work)
Supervisors
- Vajira Thambawita
- Pål Halvorsen
- Michael Riegler
References
- PhysioNet - The Research Resource for Complex Physiologic Signals
- Hugging Face datasets
- VISEM-Tracking, a human spermatozoa tracking dataset
