Awesome
MCSpatNet
Repository for Multi-Class Cell Detection Using Spatial Context Representation, ICCV 2021
<figure> <img src="./arch5.png" alt="MCSpatNet Architecture" style="width:90%"> <p align="center"> <figcaption style="font-weight:bold;text-align:center">Multi-Class Spatial Network (MCSpatNet)</figcaption> </p> </figure> <br/>-
Environment set up: refer to
environment.md
. -
Generate ground truth labels: refer to
data_preprocessing.md
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Model training and evaluation: refer to
train_and_test.md
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Pre-processed datasets: available under
datasets
. Includes: <br/> CoNSeP dataset: <br/> S. Graham, Q. D. Vu, S. E. A. Raza, A. Azam, Y-W. Tsang, J. T. Kwak and N. Rajpoot. "HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images." Medical Image Analysis, Sept. 2019 .(https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/) <br/><br/> BRCA-M2C dataset: <br/> The accompanying dataset to our paper: <br/> S. Abousamra, D. Belinsky, J. V. Arnam, F. Allard, E. Yee, R. Gupta, T. Kurc, D. Samaras, J. Saltz, C. Chen, "Multi-Class Cell Detection Using Spatial Context Representation", ICCV 2021. <br/> (https://github.com/TopoXLab/Dataset-BRCA-M2C) -
Trained models: available under
pretrained_models
. Refer topretrained_models.md
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Trained models test results: available under
pretrained_results
.
Citation
@InProceedings{Abousamra_2021_ICCV,
author = {Abousamra, Shahira and Belinsky, David and Van Arnam, John and Allard, Felicia and Yee, Eric and Gupta, Rajarsi and Kurc, Tahsin and Samaras, Dimitris and Saltz, Joel and Chen, Chao},
title = {Multi-Class Cell Detection Using Spatial Context Representation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
year = {2021},
}