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

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.

000 pixels width and 200

Dataset with examples of Artefacts in Digital Pathology.

The dataset contains 22 Whole-Slide Images, with H&E or IHC staining, showing various types and levels of defect to the slides. Annotations were made by a biomedical engineer based on examples given by an expert.

The dataset is split in different folders:

train

18 whole-slide images (extracted at 1.25x & 2.5x magnification)

All from the same Block (colorectal cancer tissue)

1/2 with H&E & 1/2 with anti-pan-cytokeratin IHC staining.

validation

3 whole-slide images (1.25x + 2.5x mag)

2 from the same Block as the training set (1 IHC, 1 H&E)

1 from another Block (IHC anti-pan-cytokerating, gastroesophageal junction lesion)

validation_tiles

patches of varying sizes taken from the 3 validation whole-slide images @1.25x magnification.

7 patches from each slide.

test

1 whole-slide image (1.25x + 2.5x mag)

From another block: IHC staining (anti-NR2F2), mouth cancer

For the train, validation and test whole-slide images, each slide has: - The RGB images @1.25x & 2.5x mag - The corresponding background/tissue masks - The corresponding annotation masks containing examples of artefacts (note that a majority of artefacts are not annotated. In total, 918 artefacts are in the train set)

For the validation tiles, the following table gives the "patch-level" supervision:

tile# Artefact(s) 00 None/Few 01 Tear&Fold 02 Ink 03 None/Few 04 None/Few 05 Tear&Fold 06 Tear&Fold + Blur 07 Knife damage 08 Knife damage 09 Ink 10 None/Few 11 Tear&Fold 12 Tear&Fold 13 None/Few 14 None/Few 15 Knife damage 16 Tear&Fold 17 None/Few 18 None/Few 19 Blur 20 Knife damage

Dataset associated to these documents:

• https://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/dab-intensity/
• https://c4science.ch/w/bioimaging_and_optics_platform_biop/image-processing/wsi_registration_fjii_qupath/

Sampled processed by the EPFL histology core facility

More documentation to come...

The Whole Slide Imaging Market report segments the industry into By Technology (Scanners, IT Infrastructure, Viewer, Image Management System), By Application (Telepathology, Cytopathology, Immunohistochemistry, Hematopathology), By End User (Academic Institute, Research, Others), and Geography (North America, Europe, Asia-Pacific, Middle East and Africa, South America). Get five years of historical data with five-year forecasts.

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.

Links to code and bioRxiv pre-print:

1. Multi-lens Neural Machine (MLNM) Code

2. An AI-assisted Tool For Efficient Prostate Cancer Diagnosis (bioRxiv Pre-print)

Digitized hematoxylin and eosin (H&E)-stained whole-slide-images (WSIs) of 40 prostatectomy and 59 core needle biopsy specimens were collected from 99 prostate cancer patients at Tan Tock Seng Hospital, Singapore. There were 99 WSIs in total such that each specimen had one WSI. H&E-stained slides were scanned at 40× magnification (specimen-level pixel size 0·25μm × 0·25μm) using Aperio AT2 Slide Scanner (Leica Biosystems). Institutional board review from the hospital were obtained for this study, and all the data were de-identified.

Prostate glandular structures in core needle biopsy slides were manually annotated and classified using the ASAP annotation tool (ASAP). A senior pathologist reviewed 10% of the annotations in each slide, ensuring that some reference annotations were provided to the researcher at different regions of the core. It is to be noted that partial glands appearing at the edges of the biopsy cores were not annotated.

Patches of size 512 × 512 pixels were cropped from whole slide images at resolutions 5×, 10×, 20×, and 40× with an annotated gland centered at each patch. This dataset contains these cropped images.

This dataset is used to train two AI models for Gland Segmentation (99 patients) and Gland Classification (46 patients). Tables 1 and 2 illustrate both gland segmentation and gland classification datasets. We have put the two corresponding sub-datasets as two zip files as follows:

1. gland_segmentation_dataset.zip
2. gland_classification_dataset.zip

Table 1: The number of slides and patches in training, validation, and test sets for gland segmentation task. There is one H&E stained WSI for each prostatectomy or core needle biopsy specimen.

	#Slides
	Train	Valid	Test	Total
Prostatectomy	17	8	15	40
Biopsy	26	13	20	59
Total	43	21	35	99
	#Patches
	Train	Valid	Test	Total
Prostatectomy	7795	3753	7224	18772
Biopsy	5559	4028	5981	15568
Total	13354	7781	13205	34340

Table 2: The number of slides and patches in training, validation, and test sets for gland classification task. There is one H&E stained WSI for each prostatectomy or core needle biopsy specimen. The gland classification datasets are the subsets of the gland segmentation datasets. GS: Gleason Score. B: Benign. M: Malignant.

	#Slides (GS 3+3:3+4:4+3)
	Train	Valid	Test	Total
Biopsy	10:9:1	3:7:0	6:10:0	19:26:1
	#Patches (B:M)
	Train	Valid	Test	Total
Biopsy	1557:2277	1216:1341	1543:2718	4316:6336

NB: Gland classification folder (gland_classification_dataset.zip) may contain extra patches, labels of which could not be identified from H&E slides. They were not used in the machine learning study.

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

The WSIs in this dataset can be viewed using the open-source software ASAP or Open Slide.

Due to the large size of the complete dataset, the data has been split up in to multiple archives.

The data from the training set:

• peso_training_masks.zip: Training masks (N=62) that have been used to train the main network of our paper. These masks are generated by a trained U-Net on the corresponding IHC slides.
• peso_training_masks_corrected.zip: A subset of the color deconvolution masks (N=25) on which manual annotations have been made. Within these regions, stain and other artifacts have been removed.
• peso_training_colordeconvolution.zip: Mask files (N=62) containing the P63&CK8/18 channel of the color deconvolution operation. These masks mark all regions that are stained by either P63 or CK8/18 in the IHC version of the slides.
• peso_training_wsi_{1-6}.zip: Zip files containing the whole slide images of the training set (N=62). Each archive contains 10 slides, excluding the last which contains 12. These images are exported at a pixel resolution of 0.48mu/pixels.

The data from the test set:

• peso_testset_regions.zip: Collection of annotation XML files with outlines of the test regions. These can be used to view the test regions in more detail using ASAP.
• peso_testset_png.zip: Export of the test set regions in PNG format (2500x2500 pixels per region).
• peso_testset_png_padded.zip: Export of the test regions in PNG format padded with a 500 pixel wide border (3500x3500 pixels per region). Useful for segmenting pixels at the border of the regions.
• peso_testset_mapping.csv: A csv file mapping files from the test set (numbered 1-160) to regions in the xml files. The csv file also contains the label (benign or cancer) for each region.
• peso_testset_wsi_{1-4}.zip: Zip files containing the whole slide images of the test set (N=40). Each archive contains 10 slides of the test set. These images are exported at a pixel resolution of 0.48mu/pixels.

This study was financed by a grant from the Dutch Cancer Society (KWF), grant number KUN 2015-7970.

If you make use of this dataset please cite both the dataset itself and the corresponding paper: https://doi.org/10.1038/s41598-018-37257-4

An anonymous whole slide image in Philips iSyntax format for running software tests on OpenPhi - Open Pathology Interface (https://zenodo.org/record/4680748#.YNnBxDqxXJU). See the repository (https://gitlab.com/BioimageInformaticsGroup/openphi/) for up to date information.

Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70% of advanced patients are with recurrent cancer and decease. Bevacizumab has been recently approved by FDA as a monotherapy for advanced ovarian cancer in combination with chemotherapy. Considering the cost, potential toxicity, and finding that only a portion of patients will benefit from these drugs, the identification of a new predictive method for the treatment of ovarian cancer remains an urgent unmet medical need. Prediction of therapeutic effects and individualization of therapeutic strategies are critical, but to the authors' best knowledge, there are no effective biomarkers that can be used to predict patient response to bevacizumab treatment for ovarian cancer. This dataset helps researchers to explore and develop methods to predict the therapeutic effect of patients with epithelial ovarian cancer to bevacizumab.

The dataset consists of de-identified 288 hematoxylin and eosin (H&E) stained whole slides with clinical information from 78 patients. The slides were collected from the tissue bank of the Tri-Service General Hospital and the National Defense Medical Center, Taipei, Taiwan. Whole Slide Images (WSIs) were acquired with a digital slide scanner (Leica AT2) with a 20x objective lens. The dimension of the ovarian cancer slides is 54342x41048 in pixels and 27.34 x 20.66mm on average. The bevacizumab treatment is effective in 162 and invalid in 126 of the dataset. Ethical approvals have been obtained from the research ethics committee of the Tri-Service General Hospital (TSGHIRB No.1-107-05-171 and No.B202005070), and the data were de-identified and used for a retrospective study without impacting patient care.

The clinicopathologic characteristics of patients were recorded by the data managers of the Gynecologic Oncology Center. Age, pre- and post-treatment serum CA-125 concentrations, histologic subtype, and recurrence, and survival status were recorded. A tumor, which is resistant to bevacizumab therapy, is defined as a measurable regrowth of the tumor or as a serum CA-125 concentration more than twice the value of the upper limit of normal during the treatment course for the bevacizumab therapy (i.e., the patient had the detectable disease or elevated CA-125 level following cytoreductive surgery combine with carboplatin/paclitaxel plus bevacizumab). A tumor, which is sensitive to bevacizumab therapy, is defined as no measurable regrowth of the tumor or as a serum CA-125 concentration under than twice the value of the upper limit of normal during the treatment course for the bevacizumab therapy.

This dataset is further described in the following publications:

• Wang et al. Weakly Supervised Deep Learning for Prediction of Treatment Effectiveness on Ovarian Cancer from Histopathology Images. Computerized Medical Imaging and Graphics. https://doi.org/10.1016/j.compmedimag.2022.102093
• Wang, CW., Chang, CC., Khalil, M.A. et al. Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer. Sci Data 9, 25 (2022). https://doi.org/10.1038/s41597-022-01127-6

This dataset represents a collection of tissue types in histological images of human gastric cancer, containing 31,096 non-overlapping images of 224x224 pixels each, extracted from H&E-stained pathological slides at Harbin Medical University Cancer Hospital. The dataset was generated by predicting tissue components in gastric cancer using annotations from a publicly available colorectal cancer dataset to create tissue heatmaps. Professional pathologists then selected 300 whole slide images with high prediction accuracy. Finally, a substantial number of images, each belonging to one of eight tissue categories (Adipose (ADI), Background (BACK), Debris (DEB), Lymphocytes (LYM), Mucus (MUC), Smooth Muscle (MUS), Normal Colon Mucosa (NORM), Cancer-associated Stroma (STR), Tumor (TUM)), were extracted from these slides.

The size of the Whole-Slide Imaging market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 6.39% during the forecast period.Whole-slide imaging is a technique wherein the whole glass microscope slide is imaged at high-resolution digital image creation. In this process, the entire slide is scanned and images obtained at microscopic resolutions that are then stitched together to form a large digital image. This large digital image may be viewed, analyzed, or shared over the Internet with specialized software.WSI is changing pathology. It affords many advantages that conventional microscopy could not offer.It allows remote consultations and second opinions, thus unifying pathologists worldwide. WSI also allows quantitative image analysis, which is the extraction of precise measurements and identification of subtle patterns, which may be hard to identify with conventional microscopy. This technology is very useful in areas such as cancer diagnosis, where accurate and timely interpretation of tissue samples is important. As WSI evolves, this will continue to play more critical roles in enhancing better patient care and advancing more medical research. Recent developments include: March 2023: Pramana, Inc., an AI-enabled health tech company modernizing the pathology sector, collaborated with PathPresenter to accelerate the enterprise adoption of digital pathology workflows. The goal of this collaboration is to ensure a seamless user experience for the labs adopting Pramana's Digital Pathology as a Service solution for whole slide image generation, as well as PathPresenter's image management/image viewing platform., March 2023: Hamamatsu, a manufacturer of photonics devices, including whole slide scanners for digital pathology, entered into a multi-year distribution agreement with Siemens Healthineers. Under the agreement, Hamamatsu is likely to provide NanoZoomer with whole slide scanners to support Siemens Healthineer's expansion into digital pathology in the Americas and Europe.. Key drivers for this market are: Growing Popularity of Virtual Slides as Compared to Physical Slides, Technological Advancements in Whole Slide Imaging; Increasing Research in Drug Discovery. Potential restraints include: Stringent Regulatory Approvals, High Cost of Whole Slide Imaging Systems. Notable trends are: Telepathology Segment is Expected to Grow Significantly Over the Forecast Period.

The dataset consists of 99 H 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.

We describe Orbit Image Analysis, an open-source whole slide image analysis tool. The tool consists of a generic tile-processing engine which allows the execution of various image analysis algorithms provided by either Orbit itself or from other open-source platforms using a tile-based map-reduce execution framework. Orbit Image Analysis is capable of sophisticated whole slide imaging analyses due to several key features. First, Orbit has machine-learning capabilities. This deep learning segmentation can be integrated with complex object detection for analysis of intricate tissues. In addition, Orbit can run locally as standalone or connect to the open-source image server OMERO. Another important characteristic is its scale-out functionality, using the Apache Spark framework for distributed computing. In this paper, we describe the use of Orbit in three different real-world applications: quantification of idiopathic lung fibrosis, nerve fibre density quantification, and glomeruli detection in the kidney.

Whole Slide Image Market Outlook

The global whole slide image market size was valued at approximately USD 800 million in 2023 and is projected to reach an estimated USD 1.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.2% during the forecast period. This robust growth is driven by the increasing adoption of digital pathology solutions, which offer enhanced accuracy and connectivity in medical diagnostics. Advances in telepathology and the growing need for more efficient pathological analysis in clinical diagnostics are major contributors to this upward trend. The market is also fueled by technological advancements in imaging technologies, improving both the quality and speed of diagnostic services.

The rising demand for early and accurate diagnosis of diseases represents a significant growth factor for the whole slide image market. The shift from traditional microscopy to digital solutions is driven by the need for improved diagnostic tools that can process large volumes of slides and provide more precise results. As healthcare providers aim to enhance patient outcomes, the adoption of whole slide imaging technology is expected to increase. Furthermore, the integration of artificial intelligence and machine learning into digital pathology is enabling more sophisticated image analysis, which is further propelling the market growth. These technologies aid pathologists in identifying patterns and anomalies, thus enhancing diagnostic capabilities and promoting wider adoption.

Another key growth driver is the increasing prevalence of chronic diseases such as cancer, which necessitates advanced diagnostic tools. Whole slide imaging provides pathologists with the ability to analyze tissue samples in high resolution, which is crucial in cancer diagnosis and research. The growing geriatric population, which is more prone to chronic conditions, further amplifies the demand for efficient diagnostic tools. Moreover, government initiatives and investments in healthcare infrastructure, especially in emerging economies, are fostering the adoption of digital pathology solutions. These initiatives are aimed at improving diagnostic accuracy and are gradually contributing to the growth of the whole slide image market.

The demand for whole slide imaging is also supported by the trend towards personalized medicine. Personalized medicine relies on detailed and precise diagnostics, which whole slide imaging can provide. By offering detailed histological data, this technology supports the development of targeted therapies and individualized treatment plans. Additionally, whole slide imaging facilitates remote consultations and second opinions through telepathology, thereby expanding its utility in the current healthcare landscape. As healthcare systems continue to adopt more patient-centric approaches, the role of whole slide imaging is expected to become increasingly significant.

Regionally, North America currently dominates the whole slide image market, attributable to the presence of advanced healthcare infrastructure and a high concentration of key market players. However, the Asia Pacific region is expected to witness the fastest growth over the forecast period. This growth is driven by the rapid advancements in healthcare infrastructure, increased healthcare spending, and a growing emphasis on precision medicine in countries like China and India. Europe is also a significant market, with increasing research activities and a focus on improving healthcare services. Latin America and the Middle East & Africa, while currently smaller markets, are gradually adopting digital pathology solutions, contributing to the overall market expansion.

Component Analysis

The whole slide image market is segmented by component into hardware, software, and services. The hardware segment includes scanners and imaging devices, which are crucial for acquiring high-resolution digital images of pathology slides. This segment holds a significant share of the market, primarily due to the ongoing advancements in imaging technology that enhance the quality and speed of slide digitization. Innovations such as higher resolution cameras and improved scanning technologies are driving the demand for advanced hardware solutions. Additionally, the increasing need for high throughput and automation in diagnostic laboratories is further fueling the growth of the hardware segment.

Software plays a pivotal role in the whole slide image market, as it is responsible for processing, analyzing, and managing digital pathology data. The software segment is witnessing rapid growth due to the integrat

The global whole slide imaging (WSI) market in pathology is flourishing, with a market size of 360 million in 2025 and a projected CAGR of 10.8% from 2025 to 2033. This growth is driven by factors such as the increasing adoption of digital pathology, the need for improved diagnostic accuracy, and the rising prevalence of chronic diseases. Major industry players include Leica Biosystems, ZEISS, Hamamatsu Photonics, 3DHISTECH Ltd., and OLYMPUS (EVIDENT). Regionally, North America dominated the market in 2025, followed by Europe and Asia Pacific. However, the Asia Pacific region is expected to witness the fastest growth over the forecast period due to the increasing healthcare expenditure and the growing awareness of digital pathology in developing countries. In terms of applications, hospitals held the largest market share in 2025, but diagnostic centers are expected to exhibit the highest growth rate during the forecast period. Brightfield imaging accounted for the largest segment in 2025, while fluorescence imaging is anticipated to grow at a faster pace over the forecast period due to its advanced capabilities and improved diagnostic accuracy. Whole Slide Imaging in Pathology: A Comprehensive Market Overview Whole Slide Imaging in Pathology Concentration & Characteristics The whole slide imaging market is highly concentrated, with a few leading players dominating the space. Leica Biosystems, ZEISS, Hamamatsu Photonics, 3DHISTECH Ltd., and OLYMPUS account for a significant share of the market. These companies focus on innovation in image acquisition, analysis, and management technologies. Regulations, such as those governing data privacy and patient confidentiality, impact the adoption of whole slide imaging. Whole Slide Imaging in Pathology Trends

Whole Slide Imaging Systems Market Outlook

The global market size for Whole Slide Imaging Systems was valued at USD 0.8 billion in 2023 and is projected to reach USD 2.3 billion by 2032, growing at a CAGR of 12.3% during the forecast period. The growth of this market is driven by advancements in digital pathology, increasing demand for high-resolution imaging in diagnostics, and the integration of artificial intelligence (AI) in pathology.

One of the key growth factors for the Whole Slide Imaging Systems market is the advancement in digital pathology. The shift from traditional microscopy to digital pathology has been transformative, enabling pathologists to easily share and analyze slides remotely. This is particularly critical in regions with a scarcity of pathologists, where digital pathology allows for rapid, remote consultations, thus improving diagnostic accuracy and speed. Additionally, the implementation of AI and machine learning algorithms in analyzing whole slide images has further enhanced diagnostic capabilities, allowing for more precise and faster identification of pathologies.

Another significant growth driver is the increasing prevalence of chronic diseases, such as cancer, that necessitate accurate and early diagnosis. Whole Slide Imaging Systems provide high-resolution images that are crucial for detecting early-stage abnormalities in tissues. This has led to a higher adoption rate of these systems in hospitals and diagnostic laboratories. Moreover, the integration of these systems with existing laboratory information systems (LIS) and electronic health records (EHR) facilitates seamless workflow and data management, thereby enhancing operational efficiency.

The rising investment in healthcare infrastructure, particularly in developing regions, is also bolstering market growth. Governments and private entities are investing heavily in modernizing healthcare facilities, which includes the adoption of advanced diagnostic tools like Whole Slide Imaging Systems. Additionally, favorable reimbursement policies and increased funding for research activities in pathology are further propelling the market. These investments not only improve diagnostic capabilities but also support extensive research activities, thereby driving the overall market growth.

Macroscopic Imaging Workstations are becoming increasingly integral to the advancement of digital pathology. These workstations provide pathologists with the ability to capture and analyze images of large tissue sections, which is crucial for comprehensive diagnostic assessments. By offering a macroscopic view, these systems complement traditional microscopic analysis, providing a broader context that can enhance diagnostic accuracy. The integration of macroscopic imaging with digital pathology platforms allows for seamless data sharing and collaboration among pathologists, further improving the efficiency of diagnostic workflows. As healthcare facilities continue to adopt advanced imaging technologies, the demand for Macroscopic Imaging Workstations is expected to rise, supporting the overall growth of the Whole Slide Imaging Systems market.

Regionally, North America held the largest market share in 2023, driven by the presence of advanced healthcare infrastructure, high adoption rates of digital pathology, and significant investments in healthcare R&D. Europe is also a significant market due to similar factors, coupled with strong government support for healthcare modernization. The Asia Pacific region is anticipated to witness the highest CAGR, owing to rapid economic growth, increasing healthcare expenditure, and a growing focus on improving diagnostic facilities.

Component Analysis

The Whole Slide Imaging Systems market can be segmented by component into hardware, software, and services. The hardware segment includes scanners and other imaging devices essential for capturing high-resolution images of pathology slides. The software segment encompasses the various digital tools used for image analysis, storage, and management. The services segment includes installation, maintenance, and training services provided by manufacturers to ensure optimal use of the imaging systems.

The hardware segment holds a significant share of the market, driven by continuous innovations in imaging technology. High-resolution scanners capable of capturing detailed images at a microscopic level are in high demand, particularly in diagnostic labora

This dataset can be used to replicate the findings in "A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images". The motivation for this paper is that increased levels of tumor infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. Our aim is to leverage computational pathology to automatically quantify TILs in standard diagnostic whole-tissue hematoxylin and eosin stained section slides (H&E slides). Our approach is to transfer an open source machine learning method for segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of our data. Our results show that improved data augmentation improves immune cell detection in H&E WSIs. Moreover, the resulting TIL quantification correlates to patient prognosis and compares favorably to the current state-of-the-art method for immune cell detection in non-small lung cancer (current standard CD8 cells in DAB stained TMAs HR 0.34 95% CI 0.17-0.68 vs TILs in HE WSIs: HoVer-Net PanNuke Model HR 0.30 95% CI 0.15-0.60). Moreover, we implemented a cloud based system to train, deploy, and visually inspect machine learning based annotation for H&E slides. Our pragmatic approach bridges the gap between machine learning research, translational clinical research and clinical implementation. However, validation in prospective studies is needed to assert that the method works in a clinical setting. The dataset is comprised of three parts: 1) Twenty image patches with and without overlays used by pathologists to manually evaluate the output of the deep learning models, 2) The models trained and subsequently used for inference in the paper, 3) the patient dataset with corresponding image patches used to clinically validate the output of the deep learning models. The tissue samples were collected from patients diagnosed between 1993 and 2003. Supplementing information was collected retrospectively in the time period 2006-2017. The images were produced in 2017.

Global sales of whole slide imaging systems are estimated at US$ 446.5 million in 2024 and have been analyzed to climb to US$ 1.08 billion by the end of 2034. The whole slide imaging system market has been forecasted to increase at 9.2% CAGR from 2024 to 2034. Advancements in digital pathology and the growing acceptance of digital imaging solutions in healthcare are key factors driving market growth.

Report Attribute	Detail
Whole Slide Imaging System Market Size (2024E)	US$ 446.5 Million
Forecasted Market Value (2034F)	US$ 1.08 Billion
Global Market Growth Rate (2024 to 2034)	9.2% CAGR
North America Market Share (2024E)	59.8%
East Asia Market Value (2024E)	US$ 110 Million
Sales of Scanners (2034F)	US$ 817.4 Million
Research Centers Segment Value (2034F)	US$ 604.7 Million
Key Companies Profiled	Hamamatsu Photonics K.K.; Roche Holding AG; NanoString Technologies; Indica Labs; Sakura Finetek; Danaher Corporation; Ventana Medical Systems, Inc.; Olympus Corporation; Leica Biosystems Nussloch GmbH; PerkinElmer, Inc.; DigiPath; 3DHISTECH Ltd.

Country-wise Insights

Attribute	United States
Market Value (2024E)	US$ 238 Million
Growth Rate (2024 to 2034)	8.6% CAGR
Projected Value (2034F)	US$ 545 Million

Attribute	Japan
Market Value (2024E)	US$ 26 Million
Growth Rate (2024 to 2034)	12.8% CAGR
Projected Value (2034F)	US$ 85 Million

Category-wise Insights

Attribute	Scanners
Segment Value (2024E)	US$ 352.8 Million
Growth Rate (2024 to 2034)	8.8% CAGR
Projected Value (2034F)	US$ 817.4 Million

Attribute	Research Centers
Segment Value (2024E)	US$ 273.5 Million
Growth Rate (2024 to 2034)	8.3% CAGR
Projected Value (2034F)	US$ 604.7 Million