Spatial transcriptomics provides cell-type labels and nuclear masks to train image-based deep learning models for nuclei analysis, achieving better segmentation accuracy and transferability to unseen organs than conventional supervised approaches.
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Leveraging Spatial Transcriptomics as Alternative to Manual Annotations for Deep Learning-Based Nuclei Analysis
Spatial transcriptomics provides cell-type labels and nuclear masks to train image-based deep learning models for nuclei analysis, achieving better segmentation accuracy and transferability to unseen organs than conventional supervised approaches.