Which Pixel to Annotate: A Label-Efficient Nuclei Segmentation Framework
Which Pixel to Annotate: A Label-Efficient Nuclei Segmentation Framework
Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H&E stained pathology images. However, it is inefficient and unnecessary to label all pixels for a dataset of nuclei images which usually contain similar and redundant patterns. Although unsupervised …