Nuclear Segmentation
11 papers with code • 1 benchmarks • 4 datasets
Latest papers with no code
Nuclear Segmentation and Classification: On Color & Compression Generalization
Since the introduction of digital and computational pathology as a field, one of the major problems in the clinical application of algorithms has been the struggle to generalize well to examples outside the distribution of the training data.
Cellular Segmentation and Composition in Routine Histology Images using Deep Learning
For the prediction of cellular composition with ALBRT on the preliminary test set, we achieved an overall $R^2$ score of 0. 53, consisting of 0. 84 for lymphocytes, 0. 70 for epithelial cells, 0. 70 for plasma and . 060 for eosinophils.
A Deep Learning Framework for Nuclear Segmentation and Classification in Histopathological Images
Nucleus segmentation and classification are the prerequisites in the workflow of digital pathology processing.
Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting
Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology (CPath).
A Standardized Pipeline for Colon Nuclei Identification and Counting Challenge
Next we constructed a baseline model HoVer-Net with cost-sensitive loss to encourage the model pay more attention on the minority classes.
Nuclei panoptic segmentation and composition regression with multi-task deep neural networks
Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology.
CoNIC: Colon Nuclei Identification and Counting Challenge 2022
The challenge encourages researchers to develop algorithms that perform segmentation, classification and counting of nuclei within the current largest known publicly available nuclei-level dataset in CPath, containing around half a million labelled nuclei.
Meta Mask Correction for Nuclei Segmentation in Histopathological Image
However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain.
Sparse Coding Driven Deep Decision Tree Ensembles for Nuclear Segmentation in Digital Pathology Images
In this way, rich image appearance models together with more contextual information are integrated by learning a series of decision tree ensembles.
NuClick: From Clicks in the Nuclei to Nuclear Boundaries
Best performing nuclear segmentation methods are based on deep learning algorithms that require a large amount of annotated data.