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ACM Multimedia BioMedia 2020 Grand Challenge Overview

Published: 12 October 2020 Publication History

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'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'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'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's lives.

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MP4 File (3394171.3416287.mp4)
The BioMedia 2020 ACM Multimedia Grand Challenge is a competition that aims to introduce multimedia researchers to different medical-related tasks that solve real-world problems. Last year, the goal was to automatically analyze images and videos taken from routine investigations of the human digestive tract to identify disease, anatomical landmarks, or other relevant findings. This year, we move the focus to assisted reproduction and how multimedia researchers can aid in the development of tools that help determine the quality of a given semen sample. The challenge lays the basis for automatic, real-time support systems for assisted 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's lives.

References

[1]
Ying Bi, Bing Xue, and Mengjie Zhang. 2018. An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming. In Applications of Evolutionary Computation, Kevin Sim and Paul Kaufmann (Eds.). 421--438.
[2]
Henny M. W. Bos and Floor B. Van Rooij. 2007. The influence of social and cultural factors on infertility and new reproductive technologies. Journal of Psychosomatic Obstetrics & Gynecology 28, 2 (2007), 65--68. https://doi.org/10.1080/01674820701447439
[3]
Violeta Chang, Laurent Heutte, Caroline Petitjean, Steffen Härtel, and Nancy Hitschfeld. 2017. Automatic classification of human sperm headmorphology. Computers in Biology and Medicine84 (2017), 205 -- 216. https://doi.org/10.1016/j.compbiomed.2017.03.029
[4]
Trevor G. Cooper, Elizabeth Noonan, Kirsten M. Vogelsong, Michael T. Mbizvo, Sigrid von Eckardstein, Jacques Auger, H.W. Gordon Baker, Hermann M. Behre, Trine B. Haugen, Thinus Kruger, and Christina Wang. 2009. World Health Organization reference values for human semen characteristics. Human Re-production Update 16, 3 (11 2009), 231--245. https://doi.org/10.1093/humupd/dmp048 arXiv: http://oup.prod.sis.lan/humupd/article-pdf/16/3/231/1791304/dmp048.pdf
[5]
Daisy Deomampo. 2019. Racialized Commodities: Race and Value in Human Egg Donation. Medical Anthropology 38, 7 (2019), 620--633. https://doi.org/10.1080/01459740.2019.1570188
[6]
Karan Dewan, Tathagato Rai Dastidar, and Maroof Ahmad. 2018. Estimation of Sperm Concentration and Total Motility From Microscopic Videos of Human Semen Samples. In Proc. of CVPR Workshops.
[7]
Muhammad Farooq and Edward Sazonov. 2017. Feature Extraction Using Deep Learning for Food Type Recognition. In Bioinformatics and Biomedical Engineering, Ignacio Rojas and Francisco Ortuño (Eds.).464--472.
[8]
Pål Halvorsen, Michael Alexander Riegler, and Klaus Schoeffmann. 2019. Medical Multimedia Systems and Applications. In Proc. of ACM MM. 2711--2713. https://doi.org/10.1145/3343031.3351319
[9]
Trine B. Haugen, Steven A. Hicks, Jorunn M. Andersen, Oliwia Witczak, Hugo L. Hammer, Rune Borgli, Pål Halvorsen, and Michael Riegler. 2019. VISEM: A Multimodal Video Dataset of Human Spermatozoa. In Proc. of MMSYS. 261--266. https://doi.org/10.1145/3304109.3325814
[10]
Steven Hicks, Michael Riegler, Pia Smedsrud, Trine B. Haugen, Kristin Ranheim Randel, Konstantin Pogorelov, Håkon Kvale Stensland, Duc-Tien Dang-Nguyen, Mathias Lux, Andreas Petlund, Thomasde Lange, Peter Thelin Schmidt, and Pål Halvorsen. 2019. ACM Multimedia BioMedia 2019 Grand Challenge Overview. In Proc. of ACM MM. 2563--2567. https://doi.org/10.1145/3343031.3356058
[11]
Steven A Hicks, Jorunn M Andersen, Oliwia Witczak, Vajira Tham-bawita, Pål Halvorsen, Hugo L Hammer, Trine B Haugen, and Michael A Riegler. 2019. Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction. Scientific Reports 9, 1 (2019), 16770. https://doi.org/10.1038/s41598-019--53217-y
[12]
Petter Jakobsen, Enrique Garcia-Ceja, Lena Antonsen Stabell, Ketil Joachim Oedegaard, Jan Oystein Berle, Steven Hicks, Vajira Thambawita, Pål Halvorsen, Ole Bernt Fasmer, and Michael Alexander Riegler. 2020. Psykose: A Motor Activity Database of Patients with Schizophrenia. https://doi.org/10.31219/osf.io/e2tzf
[13]
S. Kol. 2018. Ultra-Orthodox Jews and infertility diagnosis and treatment. Andrology 6, 5 (2018), 662--664. https://doi.org/10.1111/andr.12533
[14]
Mathias Lux, Michael Riegler, Pål Halvorsen, Konstantin Pogorelov,and Nektarios Anagnostopoulos. 2016. LIRE: Open Source VisualInformation Retrieval. In Proc. of MMSYS. Article 30, 4 pages. https://doi.org/10.1145/2910017.2910630
[15]
F Pérez-sánchez, JJ de Monserrat, and C Soler. 1994. Morphometric analysis of human sperm morphology.International journal of andrology 17, 5 (1994), 248--255.
[16]
Michael Riegler, Mathias Lux, Carsten Griwodz, Concetto Spampinato, Thomas de Lange, Sigrun L. Eskeland, Konstantin Pogorelov, Wallapak Tavanapong, Peter T. Schmidt, Cathal Gurrin, Dag Johansen, Håvard Johansen, and Pål Halvorsen. 2016. Multimedia and Medicine: Team-mates for Better Disease Detection and Survival. In Proc. of ACM MM. 968--977. https://doi.org/10.1145/2964284.2976760
[17]
Fariba Shaker, S Amirhassan Monadjemi, and Javad Alirezaie. 2017. Classification of human sperm heads using elliptic features and LDA. In 3rd International Conference on Pattern Recognition and Image Analysis(IPRIA). 151--155.
[18]
H. Tamura, S. Mori, and T. Yamawaki. 1978. Textural Features Corresponding to Visual Perception. IEEE Transactions on Systems, Man, and Cybernetics 8, 6 (1978), 460--473. https://doi.org/10.1109/TSMC.1978.4309999
[19]
Andrea Whittaker, Marcia C. Inhorn, and Francoise Shenfield. 2019. Globalised quests for assisted conception: Reproductive travel for infertility and involuntary childlessness. Global Public Health14, 12(2019), 1669--1688. https://doi.org/10.1080/17441692.2019.1627479
[20]
World Health Organization, Department of Reproductive Health and Research. 2010. WHO laboratory manual for the examination and processing of human semen.
[21]
Sophie Zadeh. 2016. Disclosure of donor conception in the era of non-anonymity: safeguarding and promoting the interests of donor-conceived individuals? Human Reproduction 31, 11 (10 2016), 2416--2420. https://doi.org/10.1093/humrep/dew240

Cited By

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  • (2023)Deep Layers Beware: Unraveling the Surprising Benefits of JPEG Compression for Image Classification Pre-processing2023 IEEE International Symposium on Multimedia (ISM)10.1109/ISM59092.2023.00033(182-185)Online publication date: 11-Dec-2023
  • (2020)A Quantitative Comparison of Different Machine Learning Approaches for Human Spermatozoa Quality Prediction Using Multimodal DatasetsProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3416285(4659-4663)Online publication date: 12-Oct-2020

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cover image ACM Conferences
MM '20: Proceedings of the 28th ACM International Conference on Multimedia
October 2020
4889 pages
ISBN:9781450379885
DOI:10.1145/3394171
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Publication History

Published: 12 October 2020

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Author Tags

  1. artificial intelligence
  2. machine learning
  3. male fertility
  4. semen analysis
  5. spermatozoa

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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View all
  • (2023)Deep Layers Beware: Unraveling the Surprising Benefits of JPEG Compression for Image Classification Pre-processing2023 IEEE International Symposium on Multimedia (ISM)10.1109/ISM59092.2023.00033(182-185)Online publication date: 11-Dec-2023
  • (2020)A Quantitative Comparison of Different Machine Learning Approaches for Human Spermatozoa Quality Prediction Using Multimodal DatasetsProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3416285(4659-4663)Online publication date: 12-Oct-2020

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