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Found 2 papers, 1 papers with code
Knowing What to Label for Few Shot Microscopy Image Cell Segmentation
In this paper, we argue that the random selection of unlabelled training target images to be annotated and included in the support set may not enable an effective fine-tuning process, so we propose a new approach to optimise this image selection process.
Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation
Deep neural networks currently deliver promising results for microscopy image cell segmentation, but they require large-scale labelled databases, which is a costly and time-consuming process.
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