Explore the TCGA Whole Slide Image (WSI) SVS files available on Kaggle, offering detailed visual representations of tissue samples from various cancer types. These high-resolution images provide valuable insights into tumor morphology and tissue architecture, facilitating cancer diagnosis, prognosis, and treatment research. Delve into the rich landscape of cancer biology, leveraging the wealth of information contained within these SVS files to drive innovative advancements in oncology. This is a dataset of WSI images downloaded from the TCGA portal.

Bone and soft tissue (BST) tumors are a heterogeneous and rare group of mesenchymal neoplasms that pose significant diagnostic challenges owing to their complex morphologies and overlapping histological features. Despite rapid advances in digital pathology, most publicly available datasets predominantly focus on common epithelial tumors and offer a limited representation of BST tumors. To address this gap, we introduced the Pathologist-Annotated Image Retrieval dataset for Bone and Soft Tissue tumors (PAIR-BST), a dataset consisting of 2,252 pathologist-annotated regions of interest derived from 470 whole slide images obtained from 268 patients diagnosed with BST tumors. Each region was curated and annotated with multi-level labels, including 33 histologic diagnoses, 11 lines of differentiation, and 6 growth patterns, enabling comprehensive characterization of the morphological heterogeneity inherent to BST tumors. Benchmark experiments have demonstrated that pathology foundation models face challenges in identifying these tumors, particularly when distinguishing between tumor subtypes and growth patterns. PAIR-BST offers a high-quality, discriminative benchmark for the development and evaluation of pathology foundation models, specifically focusing on rare mesenchymal tumors.[Metadata Availability] Additional metadata containing specific coordinates for all ROIs (Regions of Interest) within the WSI, along with labels such as diagnosis for each, is available in a separate .csv file at: https://doi.org/10.25452/figshare.plus.33383509 (also linked in the related materials below).Find related datasets in the collection at https://doi.org/10.25452/figshare.plus.c.8223469

The dataset is comprised of 38 chemically stained Whole slide image samples along with their corresponding ground truth annotated by histopathologists for 12 classes indicating skin layers (Epidermis, Reticular dermis, Papillary dermis, Dermis, Keratin), Skin tissues (Inflammation, Hair follicles, Glands), skin cancer (Basal cell carcinoma, Squamous cell carcinoma, Intraepidermal carcinoma) and background (BKG).

Computational histopathology has made significant strides in the past few years, slowly getting closer to clinical adoption. One area of benefit would be the automatic generation of diagnostic reports from H&E-stained whole slide images, which would further increase the efficiency of the pathologists' routine diagnostic workflows.

In this study, we compiled a dataset (PatchGastricADC22) of histopathological captions of stomach adenocarcinoma endoscopic biopsy specimens, which we extracted from diagnostic reports and paired with patches extracted from the associated whole slide images. The dataset contains a variety of gastric adenocarcinoma subtypes.

We trained a baseline attention-based model to predict the captions from features extracted from the patches and obtained promising results. We make the captioned dataset of 262K patches publicly available.

Purpose

The dataset was created to support research in medical image captioning — specifically, to automatically generate diagnostic text descriptions from histopathological image patches. It helps train and evaluate models that can interpret tissue morphology and produce human-like pathology reports.

Domain & Source

• Medical domain: Histopathology
• Disease focus: Gastric adenocarcinoma (a common type of stomach cancer)
• Image type: H&E-stained tissue sections
• Source images: Whole Slide Images (WSIs) split into small patches
• Magnification: 20×

Dataset Structure (PatchGastricADC22)

📁 Folder: patches_captions/patches_captions/ Contains all patch-level histopathology image files (in .jpg format). Each patch represents a cropped region (300×300 pixels) from a Whole Slide Image (WSI).

🧾 File: captions.csv Provides the mapping between image IDs and their corresponding diagnostic captions. Each row represents one unique image patch and its textual description.

🧩 CSV Columns:

id – Base ID identifying the parent WSI or case from which the patch was extracted. subtype – Indicates the histological subtype (e.g., tubular adenocarcinoma, poorly differentiated). text – Expert-written caption describing the morphological and diagnostic features visible in the patch.

Dataset Statistics 🧩 Total images (patches) ~262,777 🧪 Total WSIs (slides) 1305 🖼️ Patch size 300 × 300 pixels 🔬 Magnification 20× ✍️ Captions One per patch 🔠 Vocabulary size 344 unique words 📏 Max caption length 47 words ⚖️ Split 70% train / 10% validation / 20% test

Creation Process 1. Whole Slide Images (WSIs) were collected from gastric cancer pathology archives. 2. Each slide was divided into 300×300 patches (non-overlapping). 3. Expert pathologists annotated each patch with a short caption describing diagnostic features (cellular and structural morphology). 4. Data were consolidated into image files + a master captions.csv.

This dataset contains histopathology whole slide images (WSIs) used for microscopic tissue analysis in medical research and diagnostics. The images are provided in SVS format, a high-resolution whole-slide imaging format commonly used in digital pathology to store extremely detailed scans of tissue samples.

It is designed for tasks such as cancer detection, tissue classification, and computational pathology research, where high-quality microscopic detail is essential for training and evaluating machine learning models.

The dataset is large in size (18.6 GB) and requires the IBM Aspera Connect plugin for downloading due to the high-volume data transfer requirements.

It includes 103 to 111 indexed entries, representing individual slides or samples within the collection.

source: https://www.cancerimagingarchive.net/collection/brain-mets-lung-mri-path-segs/

The Pathology Images of Scanners and Mobilephones (PLISM) dataset was created for the evaluation of AI models’ robustness to domain shifts. PLISM is the first group-wised pathological image dataset that encompasses diverse tissue types stained under 13 H&E conditions, with multiple imaging media, including smartphones (7 scanners and 6 smartphones).The PLISM-orginal subset consists of 91 original WSIs before image registration. Color and texture in digital pathology images are affected by H&E stain conditions (e.g. Harris or Carrazi) and digitalization devices (e.g. slide scanners or smartphones), which cause inter-institutional domain shifts.The extension of each WSI file is .svs, .ndpi, or .tiff.See the other subsets of the PLISM dataset in the Collection at https://doi.org/10.25452/figshare.plus.c.6773925

Whole slide images for testing automatic analysis.

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

This dataset contains mouse colon H&E and immunohistochemistry (IHC) whole-slide images in MRXS format. The dataset includes 24 H&E slides and 72 IHC slides for ZO-1, Claudin-1, and MUC2. Slide-level metadata are provided in metadata_public.csv.

Search and license over 1,070,000 whole-slide pathology images from 362,816 patients across 11,200 tissue sites. Filter by tissue, stain, diagnosis, scanner and patient demographics, assemble a cohort, and export a CSV manifest for machine learning and computational pathology research.

The dataset consists of 99 H&E-stained whole slide skin images (WSI) - 49 abnormal and 50 normal cases. All significant abnormal findings identified are outlined and categorized into 13 types such as actinic keratosis, basal cell carcinoma and dermatofibroma. Other tissue components, such as epidermis, adnexal structures, as well as the surgical margin are delineated to create a complete histological map. In total, 16741 separate annotations have been made to segment the different tissue structures and link them to ontological information.

🔬 Gastric Cancer Histopathology Tissue Image Dataset (GCHTID) 🩺

This dataset, the Gastric Cancer Histopathology Tissue Image Dataset (GCHTID), is a valuable resource for researchers working on artificial intelligence in medical imaging, specifically for gastric cancer. It contains a large collection of expertly curated histopathology images. 🖼️

💡 Abstract

Gastric cancer (GC) remains a leading cause of cancer-related deaths globally. The diverse clinical outcomes are significantly influenced by the complex tumor microenvironment (TME). Analyzing the TME from histological images is crucial for understanding disease progression and improving treatment strategies. This dataset addresses the scarcity of well-annotated histological images of GC by providing a large collection of nearly 31,000 histological images from 300 whole slide images, meticulously categorized into 8 tissue classes relevant to the TME. The authors also validated the dataset by demonstrating its utility in training two deep learning models. This dataset serves as a valuable resource for advancing AI-driven research in gastric cancer histopathology. 🧠

https://www.nature.com/articles/s41597-025-04489-9#Sec6

📄 Description

This dataset comprises 31,096 non-overlapping images, each with a size of 224x224 pixels. These images were extracted from H&E-stained pathological slides of human gastric cancer obtained from Harbin Medical University Cancer Hospital. 🏥

The dataset focuses on the tumor microenvironment (TME) and includes images categorized into eight distinct tissue types:

• ADI: Adipose (fat tissue) 🧈
• BACK: Background (non-tissue areas) 🌫️
• DEB: Debris (cellular waste) 🗑️
• LYM: Lymphocytes (immune cells) 🛡️
• MUC: Mucus (protective secretion) 🧴
• MUS: Smooth Muscle (muscle tissue) 💪
• NORM: Normal Colon Mucosa (healthy tissue for reference) 🌱
• STR: Cancer-associated Stroma (connective tissue around the tumor) 🕸️
• TUM: Tumor (cancerous tissue) 🦠

The tissue categories were initially predicted using annotations from a publicly available colorectal cancer dataset to create tissue heatmaps. Subsequently, experienced pathologists selected 300 whole slide images with high prediction accuracy. Finally, the individual images belonging to the eight categories were extracted from these slides. 🔍

🔑 Keywords

Gastric Cancer, Histopathology, Medical Imaging, Pathological Tissue Components, Digital Pathology, Whole Slide Images, Tissue Classification

📄 Licence

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This means you are free to share and adapt the material for any purpose, even commercially, as long as you give appropriate credit, provide a link to the license, and indicate if changes were made. 📜

🔗 Citation

Shenghan Lou, Jianxin Ji, Xuan Zhang, Huiying Li, Yang Jiang, Menglei Hua, Kexin Chen, Xiaohan Zheng, Qi Zhang, Peng Han, Lei Cao, & Liuying Wang. (2024). Gastric Cancer Histopathology Tissue Image Dataset (GCHTID) [Data set]. figshare. https://doi.org/10.6084/m9.figshare.26014469.v

Dataset

This dataset was created by Lakeshprabhu Thangadurai

Released under Apache 2.0

Contents

Large set of whole-slide-images (WSI) of prostatectomy specimens with various grades of prostate cancer (PCa). More information can be found in the corresponding paper: https://doi.org/10.1038/s41598-018-37257-4

This is a representative sample from the dataset that was used to develop resolution-agnostic convolutional neural networks for tissue segmentation1 in whole-slide histopathology images.

The dataset is composed of two parts: development set and dissimilar set.

Sample images from the development set:

• breast_hne_00.tif
• breast_lymph_node_hne_00.tif
• tongue_ae1ae3_00.tif
• tongue_hne_00.tif
• tongue_ki67_00.tif

Sample images from the dissimilar set:

• brain_alcianblue_00.tif
• cornea_grocott_00.tif
• kidney_cab_00.tif
• skin_perls_00.tif
• uterus_vonkossa_00.tif

Whole Slide Imaging Market Size and Growth Outlook

Research by Growth Market Reports shows that the global Whole Slide Imaging (WSI) market size reached USD 1.29 billion in 2025, driven by rapid digitization in pathology and the growing adoption of telemedicine and remote diagnostic solutions. The market is poised for robust expansion, projected to grow at a CAGR of 14.2% from 2026 to 2034. By the end of 2034, the market is expected to attain a valuation of approximately USD 4.32 billion. This remarkable growth trajectory is primarily attributed to accelerating technological advancements in scanner hardware and AI-powered software, increased healthcare spending globally, and the rising prevalence of chronic diseases that necessitate advanced and reproducible diagnostic solutions.

One of the primary growth drivers for the Whole Slide Imaging market is the increasing demand for digital pathology solutions in clinical and research settings. Healthcare providers and research institutes are rapidly transitioning from traditional glass slides to digital slides, which offer enhanced image quality, easier storage, and improved sharing capabilities. The ability to analyze high-resolution images remotely enables pathologists to collaborate across geographies, significantly reducing diagnostic turnaround times. Moreover, the integration of artificial intelligence and machine learning tools with WSI platforms is facilitating more accurate and efficient diagnosis, further fueling market adoption. These innovations are particularly critical in oncological and rare disease diagnostics, where precision and speed are paramount. Platforms supporting DICOM-based integration of whole slide imaging systems with hospital IT infrastructure are also gaining traction, simplifying interoperability across clinical networks.

Another significant factor propelling the growth of the Whole Slide Imaging market is the continued surge in telepathology applications, building on momentum established during and after the COVID-19 pandemic. Remote diagnostic solutions became a strategic priority for healthcare systems worldwide, prompting sustained investment in digital pathology infrastructure. Telepathology, powered by WSI, enables real-time consultation and second opinions, even in regions with limited access to specialized pathologists. This trend has not only improved patient outcomes but also optimized resource allocation within healthcare systems. Regulatory approvals for digital pathology systems in major markets such as the US, the European Union, Japan, and China have further legitimized and accelerated the adoption of WSI technologies in routine clinical workflows throughout 2025 and beyond.

The expansion of Whole Slide Imaging is also supported by the growing prevalence of chronic diseases, particularly cancer, which require frequent and sophisticated pathological examinations. The aging global population and the consequent rise in disease incidence rates have placed immense pressure on pathology services to deliver faster and more accurate results. WSI addresses these challenges by enabling high-throughput analysis, digital archiving, and seamless integration with laboratory information systems. Furthermore, the increasing focus on personalized medicine and companion diagnostics is driving the need for advanced imaging solutions that can support complex biomarker analysis and tissue profiling, thereby broadening the scope of WSI applications. The emergence of high-throughput slide scanning-as-a-service models is lowering the capital barrier for smaller laboratories, making digital pathology more accessible to a wider range of institutions.

From a regional perspective, North America currently dominates the Whole Slide Imaging market, accounting for the largest share in 2025, followed by Europe and Asia Pacific. The strong presence of key market players, favorable reimbursement policies, and

The ACROBAT data set consists of 4,212 whole slide images (WSIs) from 1,153 female primary breast cancer patients. The WSIs in the data set are available at 10X magnification and show tissue sections from breast cancer resection specimens stained with hematoxylin and eosin (H&E) or immunohistochemistry (IHC). For each patient, one WSI of H&E stained tissue and at least one one, and up to four, WSIs of corresponding tissue stained with the routine diagnostic stains ER, PGR, HER2 and KI67 are available. The data set was acquired as part of the CHIME study (chimestudy.se) and its primary purpose was to facilitate the ACROBAT WSI registration challenge (acrobat.grand-challenge.org). The histopathology slides originate from routine diagnostic pathology workflows and were digitised for research purposes at Karolinska Institutet (Stockholm, Sweden). The image acquisition process resembles the routine digital pathology image digitisation workflow, using three different Hamamatsu WSI scanners, specifically one NanoZoomer S360 and two NanoZoomer XR. The WSIs in this data set are accompanied by a data table with one row for each WSI, specifying an anonymised patient ID, the stain or IHC antibody type of each WSI, as well as the magnification and microns per pixel at each available resolution level. Automated registration algorithm performance evaluation is possible through the ACROBAT challenge website based on over 37,000 landmark pair annotations from 13 annotators. While the primary purpose of this data set was the development and evaluation of WSI registration methods, this data set has the potential to facilitate further research in the context of computational pathology, for example in the areas of stain-guided learning, virtual staining, unsupervised learning and stain-independent models.

The data set consists of three subsets, the training, validation and test set, based on the ACROBAT WSI registration challenge. There are 750 cases in the training set, for each of which one H&E WSI and one to four IHC WSIs are available, with 3406 WSIs in total. The validation set consists of 100 cases with 200 WSIs in total and the test set of 303 cases with 606 WSIs in total. Both for the validation and test set, one H&E WSI as well as one randomly selected IHC WSI is available.

WSIs were anonymised by deleting the associated macro images, by generating filenames with random case IDs and by overwriting meta data fields with potentially personal information. Hamamatsu NDPI files were then converted using libvips (libvips.org/). WSIs are available as generic tiled TIFF WSIs (openslide.org/formats/generic-tiff/) at 10X magnification and lower image levels.

The data set is available for download in seven separate ZIP archives, five for the training data (train_part1.zip (71.47 GB), train_part2.zip (70.59 GB), train_part3.zip (75.91 GB), train_part4.zip (71.63 GB) and train_part5.zip (69.09 GB)), one for the validation data (valid.zip 21.79 GB) and one for the test data (test.zip 68.11 GB).

File listings and checksums in SHA1 format are available for checking archive/data integrity when downloading.

While it would be helpful to notify SND of any publications using this data set by sending an email to request@snd.gu.se, please note that this is not required to use the data.

Twenty-one privacy-sanitized H&E whole-slide images in Motic MDS format for the reproducible TumorQuantAI v0.4.0 one-slide, fast four-slide, and full 21-slide tutorials. Embedded labels, macro images, and acquisition metadata were neutralized while internal WSI pixel streams were preserved and validated. Research use only; no diagnostic annotations or clinical ground truth are included.

Anonymized whole slide image of canine mammary carcinoma, stained with H&E. File is in Aperio SVS format.

BreastPathDB BreastPathDB is a breast cancer histopathology dataset for whole-slide image based subtype classification. It contains 770 de-identified whole-slide images, including 165 H&E slides and 605 IHC slides, with clinical subtype labels, curated metadata, and patient-level train/validation/test splits. The dataset is intended for computational pathology benchmarking, weakly supervised WSI classification, and cross-cohort method development. Browse files and metadata… See the full description on the dataset page: https://huggingface.co/datasets/kmmuleelab/Breast_Pathology_Image_JPG.