Cited By
View all- Adinugroho SNakazawa A(2024)Deep learning-based sperm motility and morphology estimation on stacked color-coded MotionFlowInformatics in Medicine Unlocked10.1016/j.imu.2024.10145945(101459)Online publication date: 2024
A Quantitative Comparison of Different Machine Learning Approaches for Human Spermatozoa Quality Prediction Using Multimodal Datasets
Authors: 
Ming Feng, Kele Xu, Yin WangAuthors Info & Claims
Ming Feng, Kele Xu, Yin WangAuthors Info & ClaimsPages 4659 - 4663
Abstract
Despite remarkable advances in medical data analysis fields, they are severely restrained from the limited property of the employed single modality, usually medical imaging data. However, other modalities (such as patient-related information) should also be taken into account in the process of clinical decision. How to fully employ the multi-modal dataset is still under-explored. In this paper, we make a quantitative comparison of different machine learning approaches for the human spermatozoa quality prediction task, leveraging multiple modalities dataset. To empirically investigate the advantages and disadvantages of different machine learning approaches, we perform extensive experiments. Leveraging different features, we achieve state-of-the-art performance on most of the tasks. The obtained results show that simple models can provide better performance, which emphasizes the importance of avoiding overfitting. For the sake of reproducibility, we have released our code to facilitate the research community.
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References
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- A Quantitative Comparison of Different Machine Learning Approaches for Human Spermatozoa Quality Prediction Using Multimodal Datasets
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Information
Published In
October 2020
4889 pages
ISBN:9781450379885
DOI:10.1145/3394171
- General Chairs:
- Chang Wen Chen,
- Rita Cucchiara,
- Xian-Sheng Hua,
- Program Chairs:
- Guo-Jun Qi,
- Elisa Ricci,
- Zhengyou Zhang,
- Roger Zimmermann
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Published: 12 October 2020
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View all- Adinugroho SNakazawa A(2024)Deep learning-based sperm motility and morphology estimation on stacked color-coded MotionFlowInformatics in Medicine Unlocked10.1016/j.imu.2024.10145945(101459)Online publication date: 2024
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