Papers with Code 아카이브
Multi-tissue Nucleus Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images
PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation
HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
Micro-Net: A unified model for segmentation of various objects in microscopy images
Learning Steerable Filters for Rotation Equivariant CNNs
Mask R-CNN
Rotation equivariant vector field networks
Group Equivariant Convolutional Networks
U-Net: Convolutional Networks for Biomedical Image Segmentation
Fully Convolutional Networks for Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.