Nuclei Classification
4 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Nuclei Classification
MoNuSAC
Latest papers with no code
Measuring Feature Dependency of Neural Networks by Collapsing Feature Dimensions in the Data Manifold
Our method is based on the principle that if a model is dependent on a feature, then removal of that feature should significantly harm its performance.
A three in one bottom-up framework for simultaneous semantic segmentation, instance segmentation and classification of multi-organ nuclei in digital cancer histology
Furthermore, the framework is less complex compared to the state-of-the-art.
DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets
The experimental results suggest that the proposed method improves the classification performance of the rare type nuclei classification, while showing superior segmentation and classification performance in imbalanced pathology nuclei datasets.
Structure Embedded Nucleus Classification for Histopathology Images
Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations.
Cell nuclei classification in histopathological images using hybrid OLConvNet
To further strengthen the viability of our architectural approach, we tested our proposed methodology with state of the art deep learning architectures AlexNet, VGG16, VGG19, ResNet50, InceptionV3, and DenseNet121 as backbone networks.
Microscopic Nuclei Classification, Segmentation and Detection with improved Deep Convolutional Neural Network (DCNN) Approaches
The experimental results show that the proposed DCNN models provide superior performance compared to the existing approaches for nuclei classification, segmentation, and detection tasks.
Multi-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.
