SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification
<|reference_start|>SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification: 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.<|reference_end|>
@article{sapkota2024shmc-net:,
    title={SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology
  Classification},
    author={Nishchal Sapkota, Yejia Zhang, Sirui Li, Peixian Liang, Zhuo Zhao,
  Jingjing Zhang, Xiaomin Zha, Yiru Zhou, Yunxia Cao, Danny Z Chen},
    journal={arXiv preprint arXiv:2402.03697},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03697},
    primaryClass={cs.CV}
}