medical-1

Multi-tissue Nucleus Segmentation

3260 papers • 126 benchmarks • 313 datasets

This task has no description! Would you like to contribute one?

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in multi-tissue-nucleus-segmentation-1

Trend

Dataset

Best Model

Actions

Kumar

CoNSeP

PanNuke

Libraries

i

Use these libraries to find multi-tissue-nucleus-segmentation-1 models and implementations

Datasets

CoNSeP

PanNuke

Kumar

Subtasks

No subtasks available.

Most implemented papers

U-Net: Convolutional Networks for Biomedical Image Segmentation

O. Ronneberger, T. Brox, P. Fischer•Sun May 17 2015

It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

90626

Content

0

Paper Graph

Mask R-CNN

Kaiming He, Georgia Gkioxari, Ross B. Girshick, Piotr Dollár•Sun Mar 19 2017

This work presents a conceptually simple, flexible, and general framework for object instance segmentation, which extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.

30711 0

Paper Graph

Fully convolutional networks for semantic segmentation

Evan Shelhamer, Trevor Darrell, Jonathan Long•Thu Nov 13 2014

The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.

40974 0

Paper Graph

Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

N. Rajpoot, Y. Tsang, S. Graham, A. Azam, Q. Vu, S. Raza, J. T. Kwak•Sat Dec 15 2018

A novel convolutional neural network is presented for simultaneous nuclear segmentation and classification that leverages the instance-rich information encoded within the vertical and horizontal distances of nuclear pixels to their centres of mass to separate clustered nuclei, resulting in an accurate segmentation.

1268 0

Paper Graph

Rotation Equivariant Vector Field Networks

D. Tuia, N. Komodakis, Diego Marcos, M. Volpi•Wed Dec 28 2016

The Rotation Equivariant Vector Field Networks (RotEqNet), a Convolutional Neural Network architecture encoding rotation equivariance, invariance and covariance, is proposed and a modified convolution operator relying on this representation to obtain deep architectures is developed.

284 0

Paper Graph

Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images

N. Rajpoot, S. Graham, David B. A. Epstein•Sun Apr 05 2020

Dense Steerable Filter CNNs (DSF-CNNs) that use group convolutions with multiple rotated copies of each filter in a densely connected framework are proposed that achieve state-of-the-art performance, with significantly fewer parameters, when applied to three different tasks in the area of computational pathology.

96 0

Paper Graph

Group Equivariant Convolutional Networks

Taco Cohen, M. Welling•Tue Feb 23 2016

Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries and achieves state of the art results on CI- FAR10 and rotated MNIST.

2192 0

Paper Graph

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

M. Veta, J. Pluim, Maxime W. Lafarge, E. Bekkers, R. Duits•Wed Feb 19 2020

A framework to encode the geometric structure of the special Euclidean motion group SE(2) in convolutional networks to yield translation and rotation equivariance via the introduction of SE( 2)-group convolution layers is proposed.

85 0

Paper Graph

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

Kai Yao, Amir Hussain, Kaizhu Huang, Jie Sun•Sun Oct 31 2021

A reliable and robust method capable of dealing with data from the ‘the clinical wild’ by decoupling two simultaneous challenging tasks and taking advantage of JPFM, which can benefit from class-aware detection and class-agnostic segmentation, thus leading to a significant performance boost.

22 0

Paper Graph

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

J. T. Kwak, Tan N. N. Doan, Boram Song, T. Vuong, Kyungeun Kim•Tue Feb 08 2022

Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis. However, the task still remains challenging due to the intrinsic variations in size, intensity, and morphology of different types of nuclei. Herein, we propose a self-guided ordinal regression neural network for simultaneous nuclear segmentation and classification that can exploit the intrinsic characteristics of nuclei and focus on highly uncertain areas during training. The proposed network formulates nuclei segmentation as an ordinal regression learning by introducing a distance decreasing discretization strategy, which stratifies nuclei in a way that inner regions forming a regular shape of nuclei are separated from outer regions forming an irregular shape. It also adopts a self-guided training strategy to adaptively adjust the weights associated with nuclear pixels, depending on the difficulty of the pixels that is assessed by the network itself. To evaluate the performance of the proposed network, we employ large-scale multi-tissue datasets with 276349 exhaustively annotated nuclei. 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.

70 0

Paper Graph

Adding a benchmark result helps the community track progress.