Histopathology Datasets for Machine Learning
March 6, 2026 · View on GitHub
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-CCRCC [99] | Clear Cell Renal Cell Carcinoma | H&E | data | 783 tissue slides and 128 non-CCRCC tissue slides | images + label | classi (2) | wsi | - | 2024 |
| 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 |
| Follicle countring [98] | Ovary | H&E | data, paper, code | 643 cut slices (from 92 mice) | images + label | Object Detection (3 classes) | ROIs | Panoramic 250 Flash, Slide Scanner (3DHISTECH Ltd. HUNGARY) | 2024 |
| 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 |
| HISTAI [104] | multiple | H&E, IHC, Special | data, paper | 112,801 WSI | images + pathology reports | wsi | 20x, 40x, Leica Aperio | 2025 | |
| 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 |
| Ivy-GAP [103] | Brain | H&E, ISH | data, paper | 41cases,42tumors,947blocks，30304sections | images+RNA-seq+clinical data | wsi | 20x | 2018 | |
| 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 |
| LubLung [97] | Lung | H&E | data, paper | 23,199 patches (9 classes) | images + labels | classi | patch (87x87) | 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 | |
| PUMA [102] | Melanoma | H&E | data, paper | 206 ROIs (primary: 103, metastatic: 103) | images + nuclei and tissue annotations + context image | nuclei and tissue segmentation | patch(1024x1024) context(5120x5120) | 40x - Nanozoomer XR C12000–21/–22 | 2024 |
| 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 |
| SegLungTCGA [97] | Lung | H&E | data, paper | 454 images + file mapping info | images | segmentation (9 classes) | segmented (87x87 patches) wsi | (from TCGA) | 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 |
| SPIDER [101] | Multiple (Skin, Colorectal, Thorax) | H&E | data, paper | Skin: 159,854 patches, Colorectal: 77,182 patches, Thorax: 78,307 patches | images + labels | classification | patch (1120x1120) | 20x magnification | 2025 |
| 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 | |||
| The Digital Brain Tumour Atlas [100] | Brain | H&E | data, paper | 3,115 histological slides of 2,880 patients | images + label | classi | wsi | - | 2022 |
| 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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