Multi-tissue Nucleus Segmentation
10 papers with code • 3 benchmarks • 3 datasets
CoNSeP
PanNuke
Kumar
Most implemented papers
U-Net: Convolutional Networks for Biomedical Image Segmentation
labmlai/annotated_deep_learning_paper_implementations
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There is large consent that successful training of deep networks requires many thousand annotated training samples.
Mask R-CNN
tensorflow/models
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ICCV 2017
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
Fully Convolutional Networks for Semantic Segmentation
pochih/fcn-pytorch
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CVPR 2015
Convolutional networks are powerful visual models that yield hierarchies of features.
HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
vqdang/xy_net
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Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow.
Rotation equivariant vector field networks
di-marcos/RotEqNet
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ICCV 2017
In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image.
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear.
Group Equivariant Convolutional Networks
adambielski/pytorch-gconv-experiments
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We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries.
Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
tueimage/se2cnn
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This study is focused on histopathology image analysis applications for which it is desirable that the arbitrary global orientation information of the imaged tissues is not captured by the machine learning models.
PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification
Automatic nuclei segmentation and classification play a vital role in digital pathology.
SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images
QuIIL/Sonnet
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IEEE Journal of Biomedical and Health Informatics 2022
We show that the proposed network achieves the state-of-the-art performance in both nuclei segmentation and classification in comparison to several methods that are recently developed for segmentation and/or classification.
