Papers with Code 아카이브
Nuclear Segmentation
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
APSeg: Auto-Prompt Model with Acquired and Injected Knowledge for Nuclear Instance Segmentation and Classification
FrGNet: A fourier-guided weakly-supervised framework for nuclear instance segmentation
Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology
HoverFast: an accurate, high-throughput, clinically deployable nuclear segmentation tool for brightfield digital pathology images
Towards Large-Scale Training of Pathology Foundation Models
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear Segmentation and Classification: On Color & Compression Generalization
SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts
Cellular Segmentation and Composition in Routine Histology Images using Deep Learning
A Deep Learning Framework for Nuclear Segmentation and Classification in Histopathological Images
Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting
A Standardized Pipeline for Colon Nuclei Identification and Counting Challenge
Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting
Nuclei panoptic segmentation and composition regression with multi-task deep neural networks
SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images
CoNIC: Colon Nuclei Identification and Counting Challenge 2022
Meta Mask Correction for Nuclei Segmentation in Histopathological Image
RDCNet: Instance segmentation with a minimalist recurrent residual network
Sparse Coding Driven Deep Decision Tree Ensembles for Nuclear Segmentation in Digital Pathology Images
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
NuClick: From Clicks in the Nuclei to Nuclear Boundaries
Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework
Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature Fusion
Accurate Nuclear Segmentation with Center Vector Encoding
HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E Stained Histology Sections
Mask R-CNN
Image-to-Image Translation with Conditional Adversarial Networks
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
GRED: Graph-Regularized 3D Shape Reconstruction from Highly Anisotropic and Noisy Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.