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License: CC BY NC ND
Data sources: ZENODO
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License: CC BY NC ND
Data sources: Lunaris
ZENODO
Dataset . 2024
License: CC BY NC ND
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY NC ND
Data sources: Datacite
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NeurIPS 2022 Cell Segmentation Competition Dataset

Authors: Ma, Jun; Xie, Ronald; Ayyadhury, Shamini; Ge, Cheng; Gupta, Anubha; Gupta, Ritu; Gu, Song; +33 Authors

NeurIPS 2022 Cell Segmentation Competition Dataset

Abstract

The official data set for the NeurIPS 2022 competition: cell segmentation in multi-modality microscopy images. https://neurips22-cellseg.grand-challenge.org/ Please cite the following paper if this dataset is used in your research. @article{NeurIPS-CellSeg, title = {The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions}, author = {Jun Ma and Ronald Xie and Shamini Ayyadhury and Cheng Ge and Anubha Gupta and Ritu Gupta and Song Gu and Yao Zhang and Gihun Lee and Joonkee Kim and Wei Lou and Haofeng Li and Eric Upschulte and Timo Dickscheid and José Guilherme de Almeida and Yixin Wang and Lin Han and Xin Yang and Marco Labagnara and Vojislav Gligorovski and Maxime Scheder and Sahand Jamal Rahi and Carly Kempster and Alice Pollitt and Leon Espinosa and Tâm Mignot and Jan Moritz Middeke and Jan-Niklas Eckardt and Wangkai Li and Zhaoyang Li and Xiaochen Cai and Bizhe Bai and Noah F. Greenwald and David Van Valen and Erin Weisbart and Beth A. Cimini and Trevor Cheung and Oscar Brück and Gary D. Bader and Bo Wang}, journal = {Nature Methods}, volume={21}, pages={1103–1113}, year = {2024}, doi = {https://doi.org/10.1038/s41592-024-02233-6} } This is an instance segmentation task where each cell has an individual label under the same category (cells). The training set contains both labeled images and unlabeled images. You can only use the labeled images to develop your model but we encourage participants to try to explore the unlabeled images through weakly supervised learning, semi-supervised learning, and self-supervised learning. The images are provided with original formats, including tiff, tif, png, jpg, bmp... The original formats contain the most amount of information for competitors and you have free choice over different normalization methods. For the ground truth, we standardize them as tiff formats. We aim to maintain this challenge as a sustainable benchmark platform. If you find the top algorithms (https://neurips22-cellseg.grand-challenge.org/awards/) don't perform well on your images, welcome to send us the dataset (neurips.cellseg@gmail.com)! We will include them in the new testing set and credit your contributions on the challenge website! Dataset License: CC-BY-NC-ND

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Canada
  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    2
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Top 10%
Average
Average
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