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Nucleus segmentation: towards automated solutions
Single nucleus segmentation is a frequent challenge of microscopy image processing, since
it is the first step of many quantitative data analysis pipelines. The quality of tracking single …
it is the first step of many quantitative data analysis pipelines. The quality of tracking single …
Cat-seg: Cost aggregation for open-vocabulary semantic segmentation
Open-vocabulary semantic segmentation presents the challenge of labeling each pixel
within an image based on a wide range of text descriptions. In this work we introduce a …
within an image based on a wide range of text descriptions. In this work we introduce a …
Nuclei and glands instance segmentation in histology images: a narrative review
Examination of tissue biopsy and quantification of the various characteristics of cellular
processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance …
processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance …
Diversified and personalized multi-rater medical image segmentation
Annotation ambiguity due to inherent data uncertainties such as blurred boundaries in
medical scans and different observer expertise and preferences has become a major …
medical scans and different observer expertise and preferences has become a major …
Cx22: A new publicly available dataset for deep learning-based segmentation of cervical cytology images
The segmentation of cervical cytology images plays an important role in the automatic
analysis of cervical cytology screening. Although deep learning-based segmentation …
analysis of cervical cytology screening. Although deep learning-based segmentation …
DenseRes-Unet: Segmentation of overlapped/clustered nuclei from multi organ histopathology images
Cancer is the second deadliest disease globally that can affect any human body organ.
Early detection of cancer can increase the chances of survival in humans. Morphometric …
Early detection of cancer can increase the chances of survival in humans. Morphometric …
Nuinsseg: a fully annotated dataset for nuclei instance segmentation in h&e-stained histological images
In computational pathology, automatic nuclei instance segmentation plays an essential role
in whole slide image analysis. While many computerized approaches have been proposed …
in whole slide image analysis. While many computerized approaches have been proposed …
What a mess: Multi-domain evaluation of zero-shot semantic segmentation
While semantic segmentation has seen tremendous improvements in the past, there are still
significant labeling efforts necessary and the problem of limited generalization to classes …
significant labeling efforts necessary and the problem of limited generalization to classes …
DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions
Nuclei segmentation plays an essential role in histology analysis. The nuclei segmentation
in histology images is challenging in variable conditions (clinical wild), such as poor staining …
in histology images is challenging in variable conditions (clinical wild), such as poor staining …
A foundation model for cell segmentation
Cells are a fundamental unit of biological organization, and identifying them in imaging data–
cell segmentation–is a critical task for various cellular imaging experiments. While deep …
cell segmentation–is a critical task for various cellular imaging experiments. While deep …