NeurIPS 2021 dataset used for benchmarking feature selection for integration in H5AD format. Files contain the full raw dataset, the processed batches used to create the reference and the processed batches used as a query.Note: These files have been saved with compression to reduce file size. Re-saving without compression will reduce reading times if needed.If used, please cite:Lance C, Luecken MD, Burkhardt DB, Cannoodt R, Rautenstrauch P, Laddach A, et al. Multimodal single cell data integration challenge: Results and lessons learned. In: Kiela D, Ciccone M, Caputo B, editors. Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track. PMLR; 06--14 Dec 2022. p. 162–76. Available from: https://proceedings.mlr.press/v176/lance22a.htmlANDLuecken MD, Burkhardt DB, Cannoodt R, Lance C, Agrawal A, Aliee H, et al. A sandbox for prediction and integration of DNA, RNA, and proteins in single cells. Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). 2022 [cited 2022 Nov 8]. Available from: https://openreview.net/pdf?id=gN35BGa1Rt

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