![]() Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning. ![]() Deep-learning-based segmentation of small extracellular vesicles in transmission electron microscopy images. Nucleus segmentation across imaging experiments: the 2018 data science bowl. Deep-STORM: super-resolution single-molecule microscopy by deep learning. U-Net: deep learning for cell counting, detection, and morphometry. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 – 21st International Conference, Granada, Spain, September 16–20, 2018, Proceedings, Part II 265–273 (Springer, 2018). Cell detection with star-convex polygons. NIH Image to ImageJ: 25 years of image analysis. ![]() Quantitative digital microscopy with deep learning. Cryo-electron tomography workflows for quantitative analysis of actin networks involved in cell migration. Democratising deep learning for microscopy with ZeroCostDL4Mic. ImJoy: an open-source computational platform for the deep learning era. Ouyang, W., Mueller, F., Hjelmare, M., Lundberg, E. Ilastik: interactive machine learning for (bio)image analysis. DeepClas4Bio: Connecting bioimaging tools with deep learning frameworks for image classification. Inés, A., Domínguez, C., Heras, J., Mata, E. Content-aware image restoration: pushing the limits of fluorescence microscopy. Open-source deep-learning software for bioimage segmentation. The ImageJ ecosystem: Open-source software for image visualization, processing, and analysis. Deep learning for cellular image analysis. A bird’s-eye view of deep learning in bioimage analysis.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |