210-CELLECT: Efficient Contrastive Cell Tracking

The article introduces CELLECT, a novel deep learning method for contrastive embedding learning designed for large-scale, efficient cell tracking. The authors developed CELLECT to overcome the challenges of high-performance and high-efficiency tracking in massive three-dimensional (3D) time-lapse microscopy datasets by relying on sparse annotations instead of extensive manual segmentation. The sys...去小宇宙查看完整单集简介
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