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Nuclear Segmentation

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Showing 1–33 of 33 papers

TitleStatusHype
Towards Large-Scale Training of Pathology Foundation ModelsCode2
Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature FusionCode1
Mask R-CNNCode1
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology ImagesCode1
Perceptual Losses for Real-Time Style Transfer and Super-ResolutionCode1
Image-to-Image Translation with Conditional Adversarial NetworksCode1
SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology ImagesCode1
SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular LayoutsCode1
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and CountingCode1
HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology ImagesCode1
FrGNet: A fourier-guided weakly-supervised framework for nuclear instance segmentationCode0
Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and CountingCode0
RDCNet: Instance segmentation with a minimalist recurrent residual networkCode0
Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathologyCode0
NuClick: From Clicks in the Nuclei to Nuclear Boundaries—0
Panoptic Segmentation with an End-to-End Cell R-CNN for Pathology Image Analysis—0
Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting—0
Sparse Coding Driven Deep Decision Tree Ensembles for Nuclear Segmentation in Digital Pathology Images—0
Unpaired Image-to-Image Translation for Segmentation and Signal Unmixing—0
Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework—0
A Deep Learning Framework for Nuclear Segmentation and Classification in Histopathological Images—0
APSeg: Auto-Prompt Model with Acquired and Injected Knowledge for Nuclear Instance Segmentation and Classification—0
Cellular Segmentation and Composition in Routine Histology Images using Deep Learning—0
Classification of Tumor Histology via Morphometric Context—0
CoNIC: Colon Nuclei Identification and Counting Challenge 2022—0
Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E Stained Histology Sections—0
GRED: Graph-Regularized 3D Shape Reconstruction from Highly Anisotropic and Noisy Images—0
HoverFast: an accurate, high-throughput, clinically deployable nuclear segmentation tool for brightfield digital pathology images—0
Meta Mask Correction for Nuclei Segmentation in Histopathological Image—0
Accurate Nuclear Segmentation with Center Vector Encoding—0
A Standardized Pipeline for Colon Nuclei Identification and Counting Challenge—0
Nuclear Segmentation and Classification: On Color & Compression Generalization—0
Nuclei panoptic segmentation and composition regression with multi-task deep neural networks—0
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