SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification
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arXiv
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| Format: | Preprint |
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2024
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| author | Sapkota, Nishchal Zhang, Yejia Li, Sirui Liang, Peixian Zhao, Zhuo Zhang, Jingjing Zha, Xiaomin Zhou, Yiru Cao, Yunxia Chen, Danny Z |
| author_facet | Sapkota, Nishchal Zhang, Yejia Li, Sirui Liang, Peixian Zhao, Zhuo Zhang, Jingjing Zha, Xiaomin Zhou, Yiru Cao, Yunxia Chen, Danny Z |
| contents | Male infertility accounts for about one-third of global infertility cases. Manual assessment of sperm abnormalities through head morphology analysis encounters issues of observer variability and diagnostic discrepancies among experts. Its alternative, Computer-Assisted Semen Analysis (CASA), suffers from low-quality sperm images, small datasets, and noisy class labels. We propose a new approach for sperm head morphology classification, called SHMC-Net, which uses segmentation masks of sperm heads to guide the morphology classification of sperm images. SHMC-Net generates reliable segmentation masks using image priors, refines object boundaries with an efficient graph-based method, and trains an image network with sperm head crops and a mask network with the corresponding masks. In the intermediate stages of the networks, image and mask features are fused with a fusion scheme to better learn morphological features. To handle noisy class labels and regularize training on small datasets, SHMC-Net applies Soft Mixup to combine mixup augmentation and a loss function. We achieve state-of-the-art results on SCIAN and HuSHeM datasets, outperforming methods that use additional pre-training or costly ensembling techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03697 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification Sapkota, Nishchal Zhang, Yejia Li, Sirui Liang, Peixian Zhao, Zhuo Zhang, Jingjing Zha, Xiaomin Zhou, Yiru Cao, Yunxia Chen, Danny Z Computer Vision and Pattern Recognition Male infertility accounts for about one-third of global infertility cases. Manual assessment of sperm abnormalities through head morphology analysis encounters issues of observer variability and diagnostic discrepancies among experts. Its alternative, Computer-Assisted Semen Analysis (CASA), suffers from low-quality sperm images, small datasets, and noisy class labels. We propose a new approach for sperm head morphology classification, called SHMC-Net, which uses segmentation masks of sperm heads to guide the morphology classification of sperm images. SHMC-Net generates reliable segmentation masks using image priors, refines object boundaries with an efficient graph-based method, and trains an image network with sperm head crops and a mask network with the corresponding masks. In the intermediate stages of the networks, image and mask features are fused with a fusion scheme to better learn morphological features. To handle noisy class labels and regularize training on small datasets, SHMC-Net applies Soft Mixup to combine mixup augmentation and a loss function. We achieve state-of-the-art results on SCIAN and HuSHeM datasets, outperforming methods that use additional pre-training or costly ensembling techniques. |
| title | SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.03697 |