Less-supervised learning with knowledge distillation for sperm morphology analysis
Sperm Morphology Analysis (SMA) is pivotal in diagnosing male infertility. However, manual analysis is subjective and time-intensive. Artificial intelligence presents automated alternatives, but hurdles like limited data and image quality constraints hinder its efficacy. These challenges impede Deep...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21681163.2024.2347978 |
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| Summary: | Sperm Morphology Analysis (SMA) is pivotal in diagnosing male infertility. However, manual analysis is subjective and time-intensive. Artificial intelligence presents automated alternatives, but hurdles like limited data and image quality constraints hinder its efficacy. These challenges impede Deep Learning (DL) models from grasping crucial sperm features. A solution enabling DL models to learn sample nuances, even with limited data, would be invaluable. This study proposes a Knowledge Distillation (KD) method to distinguish normal from abnormal sperm cells, leveraging the Modified Human Sperm Morphology Analysis dataset. Despite low-resolution, blurry images, our method yields relevant results. We exclusively utilize normal samples to train the model for anomaly detection, crucial in scenarios lacking abnormal data – a common issue in medical tasks. Our aim is to train an Anomaly Detection model using a dataset comprising unclear images and limited samples, without direct exposure to abnormal data. Our method achieves Receiver ROC/AUC scores of 70.4%, 87.6%, and 71.1% for head, vacuole, and acrosome, respectively, our method matches traditional DL model performance with less than 70% of the data. This less-supervised approach shows promise in advancing SMA despite data scarcity. Furthermore, KD enables model adaptability to edge devices in fertility clinics, requiring less processing power. |
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| ISSN: | 2168-1163 2168-1171 |