Multi-tissue Nucleus Segmentation

10 papers with code • 3 benchmarks • 3 datasets

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SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images

QuIIL/Sonnet 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.

13
09 Feb 2022

PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification

kaiseem/pointnu-net 1 Nov 2021

Automatic nuclei segmentation and classification play a vital role in digital pathology.

14
01 Nov 2021

Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images

simongraham/dsf-cnn 6 Apr 2020

Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear.

73
06 Apr 2020

Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis

tueimage/se2cnn 20 Feb 2020

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.

63
20 Feb 2020

HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images

vqdang/xy_net 16 Dec 2018

Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow.

445
16 Dec 2018

Mask R-CNN

tensorflow/models ICCV 2017

Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.

76,523
20 Mar 2017

Rotation equivariant vector field networks

COGMAR/RotEqNet ICCV 2017

In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image.

54
29 Dec 2016

Group Equivariant Convolutional Networks

adambielski/pytorch-gconv-experiments 24 Feb 2016

We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries.

106
24 Feb 2016

U-Net: Convolutional Networks for Biomedical Image Segmentation

labmlai/annotated_deep_learning_paper_implementations 18 May 2015

There is large consent that successful training of deep networks requires many thousand annotated training samples.

46,278
18 May 2015

Fully Convolutional Networks for Semantic Segmentation

pochih/fcn-pytorch CVPR 2015

Convolutional networks are powerful visual models that yield hierarchies of features.

392
14 Nov 2014