SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification

• URL: http://arxiv.org/abs/2402.03697v3
• Date: Tue, 5 Mar 2024 23:55:44 GMT
• Title: SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification
• Authors: Nishchal Sapkota, Yejia Zhang, Sirui Li, Peixian Liang, Zhuo Zhao, Jingjing Zhang, Xiaomin Zha, Yiru Zhou, Yunxia Cao, Danny Z Chen
• Abstract summary: We propose a new approach for sperm head morphology classification called SHMC-Net. SHMC-Net uses segmentation masks of sperm heads to guide the morphology classification of sperm images. We achieve state-of-the-art results on SCIAN and HuSHeM datasets, outperforming methods that use additional pre-training or costly ensembling techniques.
• Score: 14.762439662731865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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