Papers Nuclei Classification
“Nuclei Classification” 태그가 달린 논문 14편 · 필터 해제
Comparative Analysis of Unsupervised and Supervised Autoencoders for Nuclei Classification in Clear Cell Renal Cell Carcinoma Images
This study explores the application of supervised and unsupervised autoencoders (AEs) to automate nuclei classification in clear cell renal cell carcinoma (ccRCC) images, a diagnostic task traditionally reliant on subjec…
ClassificationContrastive LearningDiagnosticNeural Architecture Search+1Prompting Vision-Language Model for Nuclei Instance Segmentation and Classification
Nuclei instance segmentation and classification are a fundamental and challenging task in whole slide Imaging (WSI) analysis. Most dense nuclei prediction studies rely heavily on crowd labelled data on high-resolution di…
Cell SegmentationContrastive LearningInstance SegmentationLanguage Modeling+6NuLite -- Lightweight and Fast Model for Nuclei Instance Segmentation and Classification
In pathology, accurate and efficient analysis of Hematoxylin and Eosin (H\&E) slides is crucial for timely and effective cancer diagnosis. Although many deep learning solutions for nuclei instance segmentation and classi…
Cell DetectionCell SegmentationInstance SegmentationNuclei Classification+2Measuring Feature Dependency of Neural Networks by Collapsing Feature Dimensions in the Data Manifold
This paper introduces a new technique to measure the feature dependency of neural network models. The motivation is to better understand a model by querying whether it is using information from human-understandable featu…
Disease PredictionHippocampusNuclei ClassificationCell Graph Transformer for Nuclei Classification
Nuclei classification is a critical step in computer-aided diagnosis with histopathology images. In the past, various methods have employed graph neural networks (GNN) to analyze cell graphs that model inter-cell relatio…
ClassificationNuclei ClassificationA three in one bottom-up framework for simultaneous semantic segmentation, instance segmentation and classification of multi-organ nuclei in digital cancer histology
Simultaneous segmentation and classification of nuclei in digital histology play an essential role in computer-assisted cancer diagnosis; however, it remains challenging. The highest achieved binary and multi-class Panop…
ClassificationInstance SegmentationNuclei ClassificationSegmentation+1DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets
Nuclei segmentation and classification is a significant process in pathology image analysis. Deep learning-based approaches have greatly contributed to the higher accuracy of this task. However, those approaches suffer f…
ClassificationNuclei ClassificationSegmentationDAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions
Nuclei segmentation plays an essential role in histology analysis. The nuclei segmentation in histology images is challenging in variable conditions (clinical wild), such as poor staining quality, stain variability, tiss…
Instance SegmentationNuclei ClassificationSegmentationSemantic SegmentationStructure Embedded Nucleus Classification for Histopathology Images
Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural netwo…
ClassificationGraph Neural NetworkGraph structure learningNuclei ClassificationCell nuclei classification in histopathological images using hybrid OLConvNet
Computer-aided histopathological image analysis for cancer detection is a major research challenge in the medical domain. Automatic detection and classification of nuclei for cancer diagnosis impose a lot of challenges i…
ClassificationDeep LearningNuclei ClassificationSONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images
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 …
ClassificationMulti-tissue Nucleus SegmentationNuclear SegmentationNuclei Classification+2Microscopic Nuclei Classification, Segmentation and Detection with improved Deep Convolutional Neural Network (DCNN) Approaches
Due to cellular heterogeneity, cell nuclei classification, segmentation, and detection from pathological images are challenging tasks. In the last few years, Deep Convolutional Neural Networks (DCNN) approaches have been…
ClassificationGeneral ClassificationNuclei ClassificationSegmentation+1RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification
Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better u…
ClassificationGeneral ClassificationMedical Image AnalysisNuclei ClassificationMulti-Organ Cancer Classification and Survival Analysis
Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologi…
Cancer ClassificationClassificationGeneral ClassificationNuclei Classification+2