Automatic evaluation of human oocyte developmental potential from microscopy images
暂无分享,去创建一个
[1] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[2] Subhamoy Mandal,et al. Grading of mammalian cumulus oocyte complexes using machine learning for in vitro embryo culture , 2016, 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).
[3] Vidas Raudonis,et al. Towards the automation of early-stage human embryo development detection , 2019, BioMedical Engineering OnLine.
[4] Loris Nanni,et al. Artificial intelligence techniques for embryo and oocyte classification. , 2013, Reproductive biomedicine online.
[5] Giovanna Castellano,et al. Cytoplasm Image Segmentation by Spatial Fuzzy Clustering , 2011, WILF.
[6] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[8] R. Wiaderkiewicz,et al. Semantic segmentation of human oocyte images using deep neural networks , 2021, BioMedical Engineering OnLine.
[9] Sedighe Firuzinia,et al. A robust deep learning-based multiclass segmentation method for analyzing human metaphase II oocyte images , 2021, Comput. Methods Programs Biomed..
[10] Michael Unser,et al. Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..
[11] Giovanna Castellano,et al. A Texture-Based Image Processing Approach for the Description of Human Oocyte Cytoplasm , 2010, IEEE Transactions on Instrumentation and Measurement.
[12] Ehsan Kazemi,et al. Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization , 2019, npj Digital Medicine.