/Histopathology-Datasets

Ressources of histopathology datasets

Histopathology Datasets for Machine Learning

This is a list of histopathology datasets made public for classification, segmentation, regression and/or registration tasks.

I am happy if you want to help me update and/or improve this document. I think it helps to have an overview of all the datasets available in the field.

I hope this list will help some of you.

Overview

Resources

Please find in the table below some link and information about histopathology dataset that are publicly available.

Dataset name Organs Staining Link Size Data Task WSI/Patch Other (Magnification, Scanner) year
ACDC-LungHP [1a], [1b] Lung H&E data, paper Train: 150, Test: 50 images + xml seg + classi wsi 2019
ACROBAT 2022 [66] Breast Multiple (IHC, H&E) data, paper Train: 750 train; Valid: 100; Test: 300 images (1 H&E match to 1-4 IHC) + landmarks registration wsi 40x - Hamamatsu 2022
Adipocyte [94] skin H&E data, paper 200 patches images+mask cell detection patch (120x150) 40x 2017
ADP [2] multiple multiple (most H&E) data, github, paper Train: 14.134, Valid: 1767, Test: 1767 (100 wsi) images + 57 hierarchical HTTs (histological tissue type) multi-label (3) classification (hierarchy) patch (1088x1088) 40x - Huron TissueScope LE1.2 WSI 2019
AGGC prostate H&E data, paper Subset 1: train 105, test 45; Subset2: train 37 ,test 16; Subset3: train 144, test 67 images + binary masks seg + gleason grading wsi 20x - Subset1 and Subset2: Akoya Biosciences Scanner, Subset3: each specimen is scanned by multiple scanners 2022
AML-Cytomorphology_LMU [67] Blood Wright's stain data, paper 18.365 images from 200 patients classi patch (cells) 100x - M8 digital microscope/scanner 2019
ANHIR [3] multiple (Lung, Kidney, Colon, Gastric, Breast) multiple data, paper 50+ sets image + landmarks registration patch (15k x 15k to 50k x 50k) 40x, 20x, 10x, different scanner 2019
ARCH [4] multiple multiple data, paper 4270 images + caption learn representation from text + image patch multiple 2020
BACH - ICIA2018 [5] Breast H&E data, paper 400 images (4 classes: normal 100, benign: 100, in situ carcinoma: 100, invasive carcinoma: 100) + 20 unlabeled + 10 labeled WSI (10 patients) classi + seg Patch (classi, 2048x1536) + WSI (seg) Leica SCN400 2018
BCNB [6] Breast H&E data, paper 1058 (train 0.6, valid 0.2, test 0.2) images + roi annotated + patient record binary or multiple classi wsi 2021
BCSS [7] Breast H&E data, paper 151 wsi, 20.000 patch patch + segmentation mask semantic seg patch (TCGA) 2019
Bone-Marrow-Cytomorphology [68] Marrow May-Grünwald-Giemsa/Pappenheim data, paper 171.375 cells from 945 patients images + label classi (21) patch (250x250 - single cell) 40x 2021
BRACS [62] Breast H&E data, paper 547 wsi, 4539 ROIs, 189 Patients images + label (6 subtypes tumor + normal) classi (7) wsi + patch 40x - Aperio AT2 2021
BreakHis [8] Breast H&E data, paper 7.909 (2480 benign, 5429 malignant) images + binary label + tumor type (8) (multiple magnifications: 40x, 100x, 200x, 400x) classi Patch (700x460) 40x, 100x, 200x, 400x 2016
BRCA-M2C [95] breast H&E data, paper train: 80, valid: 10, test: 30 patches images+point annotation multi-class cell detection patch (around 500x500) 20x 2021
BreCaHAD [9] Breast H&E data paper 162 images + centroid with label classi (6: mitosis, apoptosis, tumor nuclei, non-tumor nuclei, tubule, non-tubule) patch (1360x1024) 40x - Zeiss 2019
CAMEL [63] Colon data, paper 177 wsi (156 with adenoma) image + label (binary) classi patch (1280x1280) 2019
CAMELYON16 [10] Lymph node H&E data, paper Train: 270 (160 Normal, 110 with metastases); Test: 130 images + binary masks classi + seg WSI slide level analysis 2016
CAMELYON17 [11] Lymph node H&E data, paper Train: 500 (100 patients, 5 slides each); Test: 500 images + binary masks classi + seg WSI patient level analysis 2017
CAMELYON [12] Breast (Lymph node) H&E paper 1399 wsi wsi 2017
CATCH [88] Skin (Canine) H&E data, paper 350 wsi, 12.424 polygon annotations (13 classes) images + contours (JSON) seg + classi wsi 40x Aperio ScanScope CS2 (Leica) 2022
Cellseg [13] multiple multiple data, paper, github images + limited labeled patches instance (cell) segmentation wsi 2022
Chaoyang [57] Colon H&E data, github, paper Train: 111 normal, 842 serrated, 1404 adenocarcinoma, 664 adenoma, Test: 705 normal, 321 serrated, 840 adenocarcinoma, 273 adenoma samples images + label classi patch (512×512) 2021
CoCaHis [61] Colon H&E data, paper 82 (19 patients) images + mask from different annotator seg patch 2021
CoNIC 2022 [14] Colon H&E data, github, paper 4981 patch with 431.913 nuclei of 6 types image + instance seg mask + classi mask seg + classi + reg patch (256x256) 20x 2022
CoNSeP - HoVer-Net [15] Colorectal adenocarcinoma H&E data, paper Train: 27 images, Test: 14 images, 24.319 nuclei images + nuclei (location + class) instance seg + classi (7: other, inflammatory, healthy epithelial, dysplastic/malignant epithelial, figroblast, muscle, endothelial) patch (1000x1000) 40x (UHCW) 2019
CPM-15 [16] brain H&E data 15 (2905 nuclei) images + nuclei seg + label seg + classi patch (400x400, 600x1000) 20x, 40x (TCGA)
CPM-17 [17] brain H&E data, paper Train: 32, test: 32 (7570 nuclei) images + nuclei seg + label seg + classi patch (500x500 to 600x600) 20x, 40x (TCGA) 2019
CPTAC-AML Marrow, Blood data 120 images from 88 patients 40x 2020
CPTAC-BRCA Breast data 642 images from 134 patients 40x 2021
CPTAC-COAD Colon data 373 images from 106 patients 40x 2021
CPTAC-OV Ovary data 222 images from 102 patients 40x 2021
CRAG - MILD-Net [18] Colon H&E data, paper Train: 173, Valid: 40 image + segmentation instance seg patch (around 1500x1500) 20x 2019
CRCHisto [19] Colon H&E data, paper 100 images, 29.756 nuclei (10 wsi, 9 patients) images + point nuclei class label seg + classi (epithelial, inflammatory, fibroblast, miscellaneous) patch (500x500) 20x - Omnyx VL120 (UHCW) 2016
CRC-TP [20] CRC H&E data, paper 280k patches (from 20 wsi) images + tissue phenotypes classi patch 2020
CryoNuSeg [21] multiple (10: adrenal gland, larynx, lymph nodes, mediastinum, pancreas, pleura, skin, testes, thymus, and thyroid gland) H&E data, github, paper 8000 nuclei from 30 patches (from 30 wsi) images + segmentation masks + binary labels nuclei segmentation patch (512x512) 40x (from TCGA) 2021
DHMC-Kidney [85] Renal Cell Carcinoma H&E data, paper 563 wsi images + label classi wsi 20x - Aperio AT2 2021
DHMC-Lung [86] Lung Adenocarcinoma H&E data, paper 143 wsi images + label classi wsi 20x or 40x - Aperio AT2 2019
DiagSeg [58] Prostate H&E data, paper >2.6M patches (from 430 scans) 430 fully annotated scans, 4675 scans with binary diagnosis, and 46 scans with diagnosis given independently by a group of 9 histopathologists classi (256×256) patch 5x, 10x, 20x, 40x - Hamamatsu C12000-22 2021
DigestPath2019 - signet ring cell [22] multiple (Gastric, Intestine) H&E data, paper Train: 460, Test: 226 images + cell bounding boxes cell detection patch (avg 2kx2k) 40x 2019
DigestPath2019 - colonoscopy tissue segment [23] Colon H&E data, paper Train: 660, Test: 212 images + lesion annotation seg + classi (benign vs malignant) patch (avg 5kx5k) 20x 2019
DLBCL-morphology [69] Lymph Node Multiple (H&E, IHC) data, paper 52.194 patches - 246 images from 209 patients images + ROIs wsi - patch (240x240) 40x - Aperio AT2 2022
ENDO-AID [] Endometrial Carcinoma H&E data, info Test: 91 wsi images + 15 pathologists assessments grading score wsi 0.5um/px - 3DHistech P1000 2022
Gelasca et al. [26] Breast H&E data 50 images (malignant/benignant, 1.895 nuclei) + masks classi + seg Patch (896x768; 768x512)
GlaS [24] Colorectal (Gland) H&E data, paper 165 Train: 85 (37 benign, 48 malignant); Test: 80 (37 benign, 43 malignant) classi + seg Patch (diff sizes - few hundred px) 20x - Zeiss MIRAX MIDI 2015
Gleason_CNN [25] Prostate H&E data, github, paper 5 tissue microarrays (200-300 spots) images + patch and pixel annotation classi patch (3100x3100) 40x - NanoZoomer-XR Digital slide scanner, Hamamatsu 2018
GTEx Portal [77] Multiple H&E data, paper 948 patients (multiple slides per patients) images + genes + metadata
HER2 Contest [60] Breast Multiple (H&E, IHC) data, paper 172 wsi from 86 patients image + label (scoring) classi (4 classes: 0, 1+, 2+, 3+) wsi 4x-40x - Hamamatsu NanoZoomer C9600 2016
HEROHE - ECDP2020 [27a], [27b] Breast H&E data, paper Train: 359 (positive: 144, negatives: 215), Test: 150 (positive: 60, negative: 90) images + binary label classi wsi 20x - 3D Histech Pannoramic 1000 2020
HER2 tumor ROIs [70] Breast H&E data, paper 273 images + ROIs + label classi (binary) patch (512x512) 20x - Aperio ScanScope 2022
HunCRC [71] Colon H&E data, github, github, paper 101,389 patches - 200 wsi (from 200 patients) images + label classi (10) wsi - patch (512x512) 40x - 3DHistech Pannoramic 1000 2022
IMP-CRS 2024 [81a],[81b],[81c] Colorectal H&E data, paper Train 4433 wsi, Test: 900 wsi images + label classi (3) wsi 40x - Leica GT450 2024
Janowczyk et al. [28] Breast H&E data, github 143 images (12.000 nuclei) + masks semantic seg Patch (2000x2000) 40x 2015
Kather et al. [29] Colon H&E data, github, paper Train: 100k (86 wsi), Valid: 7180 (25 wsi) image + label (9 tissue type) classi patch (224x224) 2018
Kather et al. [30] Colon H&E data, data, data, github, paper seg (tumor detection) + classi (MSI detection) 2019
KIMIA Path24C [65] multiple multiple (IHC, H&E, Masson's trichrome) data, paper Train: 22.591, Valid: 1.325 from 24 wsi patch (1000x1000) 20x - TissueScope LE 1.0. 2021
Komura et al. [64] multiple (32) H&E data, paper 271.700 images + cancer type classi patch (256x256) 6 magnification (from TCGA) 2021
Kumar [31] multiple (8) H&E data, paper Train: 16 (13.372 nuclei), test same organ (4.130 nuclei): 8, test diff organ (4.121 nuclei): 6 images + nuclei seg + label seg + classi patch (1000x1000) 40x (TCGA) 2017
LC25000 [54] multiple (lung, colon) H&E data, paper 25.000 (5 classes) images + label patch (768x768) classi 60x 2019
Lizard [32] Colon H&E data, paper 495.179 nuclei images + instance seg mask seg patch 20x (DigestPath + CRAG + GlaS + PanNuke + CoNSeP + TCGA) 2021
LYON19 [33] Multiple (Breast, Colon, Protate) IHC data, paper Test: 441 ROIs - 171.166 cells images + corrdinates of cell cell detection patch Pannoramic 250Flash II scanner 2019
MBM [94] bone H&E data, paper 44 patches images+mask cell detection patch (600x600) 40x 2017
MHIST [79] colorectal polyps H&E data, paper 3,152 patches (train: 2,175; test: 977) images + annotations + annotator agreement classi (2) patch (224x224) 40x - Aperio AT2 2021
MIDOG 2021 [34] Breast H&E data, paper 200 wsi: 50 wsi / scanners - 4 scanners images + roi detection of mitotic figues wsi 2021
MIDOG 2022 [35] multiple (6 for train 10 for test) H&E data Train: 405 cases, 9501 mitotic annotation images + seg seg Patch 2022
MIDOG++ [93] multiple H&E data, paper 503 ROIs + 12k mitotic figures images + object centers detection of mitotic figures ROIs 2023
MITOS_WSI_CCMCT [89] Skin (Canine) H&E data, paper 32 wsi images + mitotic figures (45k)/ hard negatives (28k) detection of mitotic figues wsi 40x Aperio ScanScope CS2 (Leica) 2019
MITOS_WSI_CMC [90] Breast (Canine) H&E data, paper 21 wsi images + mitotic figures (14k)/ hard negatives (35k) detection of mitotic figues wsi 40x Aperio ScanScope CS2 (Leica) 2020
MoNuSAC 2020 [36] multiple (Lung, Prostate, Kidney, Breast) H&E data, paper 31.411 nuclei from 209 images images + mask instance seg + classi patch (81x113 to 1422x2162) 40x (TCGA) 2020
MoNuSeg [37a], [37b] multiple (7) H&E data, github, paper Train: 30, Test: 14 images (Train: 22.000 nuclei, Test: 7000) + masks instance seg Patch (1000x1000) 40x (from TCGA) 2018
Multi-Scanner SCC [92] Skin (Canine) H&E data, paper 44 samples á 5 scanners (220 wsi) images + contours (JSON) registration + segmentation wsi 5 scanners 2023
NADT-Prostate [72] Prostate Multiple (H&E, IHC) data, paper 1401 images from 37 patients 20x 2021
Naylor et al. [38] Breast H&E data, paper 50 images (4.022 nuclei, 11 patients) + masks seg Patch (512x512) 40x 2018
NuClick [59] Lymphocyte IHC data, paper Train: 671, Valid: 200 images + mask seg patch (256x256) 2020
NuCLS [39] Breast H&E data, paper 220.000 nuclei from 3.944 roi from 125 patients roi + bounding bx + classification nuclear detection + classi + seg patch (TCGA) 2021
OCELOT [78] Multiple (Bladder, Endometrium, Head-and-neck, Kidney, Prostate, Stomach) H&E data, paper, website 304 Whole Slide Images (WSIs) (tr:val:te 6:2:2) images + cell annotation + tissue annotation cell and tissue detection (multitask learning) patch (1024x1024) (TCGA) 2023
Osteosarcoma-Tumor-Assessment Bone H&E data 1144 images from 4 classi (3: non-tumor, viable tumor, necrosis) patch (1024x1024) 10x 2019
Ovarian Bevacizumab Response [73a], [73b] Ovary H&E data, paper, paper 288 (78 patients) images + clinical information classi (treatment effectiveness) wsi (avg 54342x41048) 20x - Leica AT2 2021
PAIP2019 [40] Liver H&E data, paper Train: 50, Valid: 10, Test: 40 images + binary mask cancer seg wsi 20x - Aperio AT2 2019
PAIP2020 [41] Colon H&E data, github Train: 47, Valid: 31, Test: 40 images + binary mask cancer seg wsi 40x - Aperio AT2 2020
PAIP2021 [42] Multiple (Colon, Prostate, Pancreas) H&E data, paper Train: 150, Valid: 30, Test: 60 wsi + xml gt semantic seg wsi 20x - Aperio AT2 2021
PAIP2023 multiple organ H&E data 2023
The PANDA challenge [43] Prostate H&E data, paper Train: 10.616, Valid: 393, Internal test: 545, External test: 1071 images + label classi wsi slide level analysis 2020
Pan-tumor T-lymphocyte dataset [91] Multiple IHC (CD3) data, paper 92 ROIs images + cell annotations detection + classification wsi 40x NanoZoomer 2.0-HT (Hamamatsu) 2023
SegPath [87] multiple H&E data, paper 158,687 patches images + label + mask semantic seg patch 20x - Zeiss MIRAX MIDI 2023
PanNuke [44a], [44b] multiple (19) H&E data, github, paper, paper 189.744 nuclei (from >20k wsi) images + nuclei (position + classi: neoplastic, connective, non-neoplastic epithelial, dead, inflammatory) instance seg + classi patch 40x 2019
PatchCamelyon [45a], [45b] Lymph node H&E data, github paper 327.680 images + binary label classi Patch (96x96) 10x 2018
PATHVQA [80] Multiple Multiple data, paper, github 32,799 open-ended questions from 4,998 images image + question + answer VQA patch/image 2020
Post-NAT-BRCA [74] Breast H&E data, paper 96 images from 54 patients images + clinical info + annotation tumor cellularity and cell labels wsi 20x - Aperio 2021
Prostate Fused-MRI-Pathology [83] Prostate H&E data 114 images from 16 patients images + tumor Annotations + mpMRI wsi 20x - Aperio 2016
RINGS [96] prostate H&E data , paper train: 1000 , test: 500 with 18'851 glands images+mask gland segmentation and tumor segmentation patch (1500x1500) 40x 2021
SegPC-2021 [46a], [46b], [46c], [46d] Blood Jenner-Giemsa data, github, report 775 images, Train: 298, Valid: 200, Test: 277 images + nucleus and cytoplasma plasma cell segmentation 2021
SICAPv2 [55] Prostate H&E data, paper 155 (from 95 patients) images + global Gleason scores and patch-level Gleason grades classi wsi 40x - Ventana iScan Coreo 2020
SLN-Breast [75] Breast H&E data, paper 130 wsi from 78 patients images + binary label classi (binary - cancer/no cancer) wsi 20x - Leica Aperio AT2 2021
SPIE-AAPM_NCI BreastPathQ [47] Breast H&E data, paper 2579 patch from 96 wsi (64 patients) images + score regression patches 20x 2019
TCGA [48] Multiple H&E data, data > 11k WSI
TCGA-TIL-WSI [76] Multiple (13) H&E data, github, paper 5200 (from TCGA) 2019
TIGER [49] Breast H&E data, paper, github, github WSIROIS: 195 wsi, WSIBULK: 93, WSITILS: 82 images + rois + label (7) detection + segmentation + TILs scoring wsi (from TCGA, RUMC, JB) 2022
TissueNet Uterine cervix H&E data, github 1,016 WSIs; 5,926 patches (1200x1200 px) images + annotation + metadata + labels classi (4) wsi + patches MIRAX, Aperio, Hamamatsu 2020
TNBC [50] Breast H&E data, data, paper 50 images, 4022 cells (11 patients) images + nuclei seg + label seg + classi patch (512x512) 40x - Philips Ultra Fast Scanner (Curie Inst.) 2019
Tolkach Y. et al. [84] oesophageal adenocarcinomas H&E data, paper UKK1: 34,704 patches from 22 wsi (20 patients); WNS: 121,642 patches from 62 wsi (15 patients); CHA: 32,796 patches from 214 wsi (69 patients); TCGA:178,187 patches from 22 wsi (22 patients) images + label classi (11) patch(256x256) 40x - Nanozoomer S360 2023
TUPAC16 [51] Breast H&E data, paper 500 images + label classi (wsi level) WSI 40x (from TCGA) 2019
TUPAC16 - aux [52] Breast - mitoses H&E data 73 images + locations seg patch 40x (from TCGA) Leica SCN400 2019
UniToPatho [56] Colon H&E data, paper 9.536 from 292 wsi images + label (6 classes) classi patch 20x - Hamamatsu Nanozoomer S210 2021
UPENN-GBM [82] glioblastoma H&E data,paper 71 wsi from 34 patients images + clinical data + mpMRI WSI 40x 2022
VisioMel Melanoma H&E data, code train: 1342 wsi, test: 600, valid: 1200, 16 WSIs annotated images + annotation + clinical metadata + label classi (2) 2023
WSSS4LUAD [53] Lung H&E data, paper 87 (Train: 53, valid: 12, Test: 12) Train: 10.091 patches, Valid: 40 patches, Test: 80 patches; image level for train, pixel level for test/valid tissue semantic seg wsi (67 GDPH, 20 TCGA) 2021

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Marie (Duc) Stettler