@misc{15909,
  keywords = {Machine learning, artificial intelligence, semen analysis, spermatozoa, male fertility},
  author = {Steven Hicks and Vajira Thambawita and Hugo Hammer and Trine Haugen and Jorunn Andersen and Oliwia Witczak and P{\r a}l Halvorsen and Michael Riegler},
  title = {ACM Multimedia BioMedia 2020 Grand Challenge Overview},
  abstract = {The BioMedia 2020 ACM Multimedia Grand Challenge is the second in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year{\textquoteright}s challenge, participants are asked to develop algorithms that automatically predict the quality of a given human semen sample using a combination of visual, patient-related, and laboratory-analysis-related data. Compared to last year{\textquoteright}s challenge, participants are provided with a fully multimodal dataset (videos, analysis data, study participant data) from the field of assisted human reproduction. The tasks encourage the use of the different modalities contained within the dataset and finding smart ways of how they may be combined to further improve prediction accuracy. For example, using only video data or combining video data and patient-related data. The ground truth was developed through a preliminary analysis done by medical experts following the World Health Organization{\textquoteright}s standard for semen quality assessment. The task lays the basis for automatic, real-time support systems for artificial reproduction. We hope that this challenge motivates multimedia researchers to explore more medical-related applications and use their vast knowledge to make a real impact on people{\textquoteright}s lives.},
  year = {2020},
  journal = {Proceedings of the 28th ACM International Conference on Multimedia},
  pages = {4655{\textendash}4658},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  isbn = {9781450379885},
  url = {https://doi.org/10.1145/3394171.3416287},
  doi = {10.1145/3394171.3416287},
}