Table 5 A summary of weakly supervised... | Emerald Publishing

Table 5

A summary of weakly supervised nuclei segmentation methods.

Ref.	Dataset	Methods	Pre-Processing	Post-Processing
[106]	MoNuSeg, TNBC	ResNet-50 backbone segmentation net supervised by auxiliary PseudoEdgeNet	Label assignment -Voronoi and distance transform	Thresholding
[74]	Lung Cancer Dataset, Kumar Dataset	Semi-supervised nuclei detection followed by weakly supervised segmentation (using ResNet backbone U-Net)	Color normalization, patch extraction, data augmentation, ResNet-34 encoder pretrained, Voronoi and K-means cluster labeling	-
[75]	Lung Cancer Dataset, Kumar Dataset	Uncertainty prediction from Bayesian CNN followed by normal CNN trained with partial points and mask labels	Color normalization, patch extraction, data augmentation	-
[95]	MoNuSeg, TNBC	Coarse segmentation using self supervision followed by fine segmentation with contour sensitive constraint	Point distance map and Voronoi edge distance map generation	-
[54]	MoNuSeg, CPM-17	Co-trained U-Net based Segmentation-Colorization Network	Patch extraction, data augmentation, H-component extraction, Voronoi and K-means cluster labeling	-
[36]	MoNuSeg	GAN based nuclei centroid detection followed by peak region backpropagation	Stain normalization, patch extraction	Graph cuts
[58]	Kumar, TNBC, MoNuSeg	Conditional SinGAN based training data augmentation from selected patches followed by Mask RCNN for semi-supervised segementation	Patch extraction, data augmentation	-

Ref.	Dataset	Methods	Pre-Processing	Post-Processing
[106]	MoNuSeg, TNBC	ResNet-50 backbone segmentation net supervised by auxiliary PseudoEdgeNet	Label assignment -Voronoi and distance transform	Thresholding
[74]	Lung Cancer Dataset, Kumar Dataset	Semi-supervised nuclei detection followed by weakly supervised segmentation (using ResNet backbone U-Net)	Color normalization, patch extraction, data augmentation, ResNet-34 encoder pretrained, Voronoi and K-means cluster labeling	-
[75]	Lung Cancer Dataset, Kumar Dataset	Uncertainty prediction from Bayesian CNN followed by normal CNN trained with partial points and mask labels	Color normalization, patch extraction, data augmentation	-
[95]	MoNuSeg, TNBC	Coarse segmentation using self supervision followed by fine segmentation with contour sensitive constraint	Point distance map and Voronoi edge distance map generation	-
[54]	MoNuSeg, CPM-17	Co-trained U-Net based Segmentation-Colorization Network	Patch extraction, data augmentation, H-component extraction, Voronoi and K-means cluster labeling	-
[36]	MoNuSeg	GAN based nuclei centroid detection followed by peak region backpropagation	Stain normalization, patch extraction	Graph cuts
[58]	Kumar, TNBC, MoNuSeg	Conditional SinGAN based training data augmentation from selected patches followed by Mask RCNN for semi-supervised segementation	Patch extraction, data augmentation	-