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Spectral clustering method is applied on the output of the last SpaNet, which utilizes the nuclear mask and the Gaussian-like detection map for determining the connected components and associated cluster identifiers, respectively.
Automated detection and segmentation of individual nuclei in histopathology images is important for cancer diagnosis and prognosis.
SOTA for Nuclear Segmentation on Cell17
Nuclear segmentation is important and frequently demanded for pathology image analysis, yet is also challenging due to nuclear crowdedness and possible occlusion.
The morphological clues of various cancer cells are essential for pathologists to determine the stages of cancers.
#2 best model for Nuclear Segmentation on Cell17
The fusion relies on integrating of networks that learn region- and boundary-based representations.
Analysis of microscopy images can provide insight into many biological processes.
Image-based classification of tissue histology, in terms of different components (e. g., normal signature, categories of aberrant signatures), provides a series of indices for tumor composition.