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To use this dataset for your research, please cite the following preprint. Full-paper will be available soon.
Citation:
@article{thambawita2022visem, title={VISEM-Tracking: Human Spermatozoa Tracking Dataset}, author={Thambawita, Vajira and Hicks, Steven A and Stor{\aa}s, Andrea M and Nguyen, Thu and Andersen, Jorunn M and Witczak, Oliwia and Haugen, Trine B and Hammer, Hugo L, and Halvorsen, P{\aa}l and Riegler, Michael A}, journal={arXiv preprint arXiv:2212.02842}, year={2022} } ☝️ ☝️ ☝️
Motivation and background
Manual evaluation of a sperm sample using a microscope is time-consuming and requires costly experts who have extensive training. In addition, the validity of manual sperm analysis becomes unreliable due to limited reproducibility and high inter-personnel variations due to the complexity of tracking, identifying, and counting sperm in fresh samples. The existing computer-aided sperm analyzer systems are not working well enough for application in a real clinical setting due to unreliability caused by the consistency of the semen sample. Therefore, we need to research new methods for automated sperm analysis.
Target group
The task is of interest to researchers in the areas of machine learning (classification and detection), visual content analysis, and multimodal fusion. Overall, this task is intended to encourage the multimedia community to help improve the healthcare system through the application of their knowledge and methods to reach the next level of computer and multimedia-assisted diagnosis, detection, and interpretation.
Class Label Mapping
sperm: 0 cluster: 1 small or pinhead: 2
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