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Found 9 papers, 5 papers with code
Cell Graph Transformer for Nuclei Classification
Nuclei classification is a critical step in computer-aided diagnosis with histopathology images.
Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images
Afterward, we train a separate segmentation model for each category using the images in the corresponding category.
Diffusion-based Data Augmentation for Nuclei Image Segmentation
Therefore, augmenting a dataset with only a few labeled images to improve the segmentation performance is of significant research and application value.
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
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.
Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework
Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H\&E stained pathology images.
Fast Heterogeneous Federated Learning with Hybrid Client Selection
We present a novel clustering-based client selection scheme to accelerate the FL convergence by variance reduction.
Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection
However, due to the large variance of the selected subset's update, prior selection approaches with a limited sampling ratio cannot perform well on convergence and accuracy in heterogeneous FL.
Dynamic-OFA: Runtime DNN Architecture Switching for Performance Scaling on Heterogeneous Embedded Platforms
However, the training process of such dynamic DNNs can be costly, since platform-aware models of different deployment scenarios must be retrained to become dynamic.
