Table 2.
Digital pathology public datasets summary
| Dataset name | Type | Task | Tissue | Staining method | ImageNum | Year | Refsa | Link |
|---|---|---|---|---|---|---|---|---|
| TCGA | Dataset | — | — | — | — | 2008 | [167] | https://gdc.cancer.gov/ |
| ICPR 2012 | Challenge | Automated detection of mitotic cells | Breast | H&E | 5 | 2012 | [170] | http://ludo17.free.fr/mitos_2012/ |
| MITOS & ATYPIA 2014 | Challenge | Detection of mitosis and evaluation of nuclear atypia score | Breast | H&E | 32 | 2014 | [171] | https://mitos-atypia-14.grand-challenge.org/ |
| GlaS | Challenge | Segmentation of glands | Colon | H&E | 165 | 2015 | [172] | https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest |
| CAMELYON16 | Challenge | Detection of micro- and macro-metastases in lymph node digitized images | Breast | H&E | 400 | 2016 | [173] | https://camelyon16.grand-challenge.org/Home/ |
| TUPAC | Challenge | Predicting tumor proliferation fraction | Breast | H&E | 500 and auxiliary dataset | 2016 | [174] | https://tupac.grand-challenge.org/ |
| CAMELYON17 | Challenge | Detection and classification of breast cancer metastases in lymph nodes | Breast | H&E | 1399 | 2017 | [175] | https://camelyon17.grand-challenge.org/Home/ |
| LSVM_CTXT | Dataset | Subtype classification of ovarian cancer | Ovarian | H&E | 133 | 2017 | [93] | http://www.sfu.ca/~abentaie/LSVM_CTXT/LSVM_CTXT.html |
| MCSU | Dataset | Quantification of tumor hypoxia | Eight tumors | H&E and (immuno-)fluorescence | 178 | 2017 | [151] | https://cs.adelaide.edu.au/~carneiro/humboldt.html |
| nucleisegmentation | Dataset | Segmentation of nuclei | Seven organs | H&E | 30 | 2017 | [41] | https://nucleisegmentationbenchmark.weebly.com/ |
| BACH | Challenge | Classification of the four types of breast cancer | Breast | H&E | 400 | 2018 | [176] | https://iciar2018-challenge.grand-challenge.org/Home/ |
| Pcam | Dataset | Differentiation of the presence for metastatic tissue | Lymph node | H&E | 327 680 | 2018 | [177] | https://github.com/basveeling/pcam |
| ANHIR | Challenge | Registration of stained images by different biomarkers | Lung, kidney, breast etc. | H&E, Cc10, proSPC, Ki67 etc. | 355 | 2019 | [178] | https://anhir.grand-challenge.org/Intro/ |
| BCSS | Dataset | Semantic segmentation of breast cancer | Breast | H&E | 151 | 2019 | [179] | https://github.com/PathologyDataScience/BCSS |
| DigestPath2019 | Challenge | Detection of signet ring cells & segmentation and classification of colonoscopy tissue | Gastric mucosa, intestine and colon | H&E | 687 & 872 | 2019 | [180] | https://digestpath2019.grand-challenge.org/Home/ |
| Gleason 2019 | Challenge | Automated Gleason grading | Prostate | H&E | 333 | 2019 | [81] | https://gleason2019.grand-challenge.org/Home/ |
| MITOS_WSI_CCMCT | Dataset | Detection of mitosis | Skin(dog) | H&E (manually expert labeled dataset) | 32 | 2019 | [181] | https://github.com/DeepPathology/MITOS_WSI_CCMCT |
| NCT-CRC-HE-100 K | Dataset | Automated tissue decomposition | Colon | H&E | 100 000 | 2019 | [98] | http://dx.doi.org/10.5281/zenodo.1214456 |
| PAIP 2019 | Challenge | Detection of liver cancer | Liver | H&E | 100 | 2019 | [182] | https://paip2019.grand-challenge.org/Home/ |
| HEROHE | Challenge | Classification of her2-positive and negative | Breast | H&E | 360 | 2020 | [183] | https://ecdp2020.grand-challenge.org/Home/ |
| MITOS_WSI_CMC | Dataset | Detection of mitosis | Breast(dog) | H&E (manually expert labeled dataset) | 21 | 2020 | [184] | https://github.com/DeepPathology/MITOS_WSI_CMC/ |
| MoNuSAC | Challenge | Segmentation of nuclei of multiple cell-types | Lung, prostate, kidney, and breast | H&E | 71 | 2020 | [185] | https://monusac-2020.grand-challenge.org/Home/ |
| PANDA | Challenge | Automated Gleason grading | Prostate | H&E | 11 000 | 2020 | [186] | https://www.kaggle.com/c/prostate-cancer-grade-assessment/overview |
| BCNB | Dataset | Prediction of the metastatic status of ALN, histological grading and molecular subtype etc. | Breast | H&E and clinical characteristics | 1058 | 2021 | [187] | https://bcnb.grand-challenge.org/Home/ |
| MIDOG 2021 | Challenge | Identification of mitotic figure | Breast | H&E | 150 | 2021 | [188] | https://imig.science/midog/ |
| NuCLS | Dataset | Classification, localization and segmentation of the cell nucleus | Breast | H&E | 125 | 2021 | [189] | https://nucls.grand-challenge.org/NuCLS/ |
| PAIP 2021 | Challenge | Detection of perineural invasion in multiple organ cancer | Colon, prostate and pancreas | H&E | 240 | 2021 | [190] | https://paip2021.grand-challenge.org/Home/ |
| WSSS4LUAD | Challenge | Prediction of three tissue types | Lung | H&E | 87 | 2021 | [191] | https://wsss4luad.grand-challenge.org/WSSS4LUAD/ |
| CoNIC | Challenge | Segmentation and classification of nuclear and prediction of cellular composition | Colon | H&E | 4981 | 2022 | [192] | https://conic-challenge.grand-challenge.org/Home/ |
aThe reference numbers are consistent with the text.