medical-3

Nuclear Segmentation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in nuclear-segmentation-7

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Dataset

Best Model

Actions

Cell17

Libraries

i

Use these libraries to find nuclear-segmentation-7 models and implementations

Datasets

CoNSeP

2018 Data Science Bowl

Cell

MoNuSAC

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Most implemented papers

Image-to-Image Translation with Conditional Adversarial Networks

Phillip Isola, Alexei A. Efros, Jun-Yan Zhu, Tinghui Zhou•Sun Nov 20 2016

Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.

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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

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Perceptual Losses for Real-Time Style Transfer and Super-Resolution

Justin Johnson, Li Fei-Fei, Alexandre Alahi•Sat Mar 26 2016

This work considers image transformation problems, and proposes the use of perceptual loss functions for training feed-forward networks for image transformation tasks, and shows results on image style transfer, where aFeed-forward network is trained to solve the optimization problem proposed by Gatys et al. in real-time.

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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

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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.

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Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature Fusion

Weidong (Tom) Cai, Yang Song, Dongnan Liu, Donghao Zhang, C. Zhang, Fan Zhang, L. O’Donnell•Wed Jul 31 2019

This work proposes a panoptic segmentation model which incorporates an auxiliary semantic segmentation branch with the instance branch to integrate global and local features and designs a feature map fusion mechanism in the instance Branch and a new mask generator to prevent information loss.

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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

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.

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Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting

Angelica I. Avilés-Rivero, Lihao Liu, Chenyang Hong, C. Schonlieb•Mon Feb 28 2022

This work addresses the problem of automated nuclear segmentation, classification, and quantification from Haematoxylin and Eosin stained histology images, which is of great relevance for several downstream computational pathology applications with a simultaneous semantic and instance segmentation framework.

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SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts

F. Minhas, Srijay Deshpande, Muhammad Dawood, N. Rajpoot•Tue Dec 27 2022

A novel framework that can construct realistic and high-quality histology images from user-defined cellular layouts along with annotated cellular boundaries is proposed and it is demonstrated that augmenting limited real data with the synthetic data generated by the framework can significantly boost prediction performance of the cellular composition prediction task.

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