Cell Detection
45 papers with code • 4 benchmarks • 4 datasets
Cell Detection
Benchmarks
These leaderboards are used to track progress in Cell Detection
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PanNuke
Fluo-N2DH-GOWT1
Most implemented papers
On Complex Valued Convolutional Neural Networks
Doyosae/Deep-Complex-Networks
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The resulting model is shown to be a restricted form of a real valued CNN with twice the parameters.
Cell Detection in Microscopy Images with Deep Convolutional Neural Network and Compressed Sensing
yaoxuexa/CNNCS
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In this paper, we seek a different route and propose a convolutional neural network (CNN)-based cell detection method that uses encoding of the output pixel space.
Cell Detection with Star-convex Polygons
Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications.
Small Object Detection via Pixel Level Balancing With Applications to Blood Cell Detection
hubindijun/faster-rcnn-plb-PyTorch
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Frontiers in Physiology 2022
This method can perform well with blood cell detection in our experiments.
Contour Proposal Networks for Biomedical Instance Segmentation
FZJ-INM1-BDA/celldetection
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We construct CPN models with different backbone networks, and apply them to instance segmentation of cells in datasets from different modalities.
TABBIE: Pretrained Representations of Tabular Data
Existing work on tabular representation learning jointly models tables and associated text using self-supervised objective functions derived from pretrained language models such as BERT.
Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap
hyeonwoocho7/Cell_Detection-MICCAI
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We propose an unsupervised domain adaptation method for cell detection using the pseudo-cell-position heatmap, where a cell centroid becomes a peak with a Gaussian distribution in the map.
Multi-Class Cell Detection Using Spatial Context Representation
topoxlab/mcspatnet
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ICCV 2021
In digital pathology, both detection and classification of cells are important for automatic diagnostic and prognostic tasks.
A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images
Our approach is to transfer an open source machine learning method for segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of our data.
Self-supervised pseudo-colorizing of masked cells
roydenwa/cell-centroid-former
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Self-supervised learning, which is strikingly referred to as the dark matter of intelligence, is gaining more attention in biomedical applications of deep learning.
