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Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Marcello Malpighi and the Evolution of Embryology, by Howard B. Adelmann, 5 vols. (pp. 2475) 4to, 16 plates (11 col.), map, Cornell University Press and Oxford University Press, 1966. £70.
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A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association
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A roadmap for AI-aided digital twins in computational nuclear oncology: A position paper by the EANM
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Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade
inferred← citeshttps://doi.org/10.1016/j.ejmp.2021.02.006openalex-snapshot
AI applications to medical images: From machine learning to deep learning
inferred← citeshttps://doi.org/10.1016/j.ejmp.2025.105066openalex-snapshot
Using HistoQC to predict disagreement on human epidermal growth factor receptor 2 (HER2) assessment
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.126125openalex-snapshot
Channel transformer based multi field-of-view model to detect tumor spread through air space in histopathological images
inferred← citeshttps://doi.org/10.1016/j.future.2022.10.025openalex-snapshot
The vendor-agnostic EMPAIA platform for integrating AI applications into digital pathology infrastructures
inferred← citeshttps://doi.org/10.1016/j.heliyon.2023.e13171openalex-snapshot
Deep-learning based breast cancer detection for cross-staining histopathology images
inferred← citeshttps://doi.org/10.1016/j.hpb.2024.12.012openalex-snapshot
Postoperative pancreatic fistula risk assessment using digital pathology based analyses at the parenchymal resection margin of the pancreas – Results from the randomized multicenter RECOPANC trial
inferred← citeshttps://doi.org/10.1016/j.iliver.2024.100082openalex-snapshot
Artificial intelligence-based pathological analysis of liver cancer: Current advancements and interpretative strategies
inferred← citeshttps://doi.org/10.1016/j.imu.2022.100853openalex-snapshot
Evaluating generic AutoML tools for computational pathology
inferred← citeshttps://doi.org/10.1016/j.inffus.2025.103737openalex-snapshot
ConVLM: Context-guided vision-language model for fine-grained histopathology image classification
inferred← citeshttps://doi.org/10.1016/j.isci.2025.112980openalex-snapshot
Comprehensive application of artificial intelligence in colorectal cancer: A review
inferred← citeshttps://doi.org/10.1016/j.jasc.2022.02.001openalex-snapshot
Is it real or not? Toward artificial intelligence-based realistic synthetic cytology image generation to augment teaching and quality assurance in pathology
inferred← citeshttps://doi.org/10.1016/j.jasc.2022.10.001openalex-snapshot
Are synthetic cytology images ready for prime time? A comparative assessment of real and synthetic urine cytology images
inferred← citeshttps://doi.org/10.1016/j.jceh.2026.103608openalex-snapshot
Digital Pathology–Enabled Artificial Intelligence for Fibrosis Assessment in Metabolic Dysfunction–Associated Steatohepatitis: Needs, Current Progress, and Barriers
inferred← citeshttps://doi.org/10.1016/j.jhep.2023.10.015openalex-snapshot
Artificial intelligence-assisted digital pathology for non-alcoholic steatohepatitis: current status and future directions
inferred← citeshttps://doi.org/10.1016/j.jhep.2025.07.003openalex-snapshot
The barriers to uptake of artificial intelligence in hepatology and how to overcome them
inferred← citeshttps://doi.org/10.1016/j.jocn.2026.112199openalex-snapshot
Vascular wall structural heterogeneity and hemorrhagic complications during endovascular therapy for brain arteriovenous malformations
inferred← citeshttps://doi.org/10.1016/j.jpi.2022.100101openalex-snapshot
A tool for federated training of segmentation models on whole slide images
inferred← citeshttps://doi.org/10.1016/j.jpi.2023.100299openalex-snapshot
Artificial intelligence-based multi-class histopathologic classification of kidney neoplasms
inferred← citeshttps://doi.org/10.1016/j.jpi.2023.100335openalex-snapshot
Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey
inferred← citeshttps://doi.org/10.1016/j.jpi.2023.100343openalex-snapshot
Computational pathology in the identification of HER2-low breast cancer: Opportunities and challenges
inferred← citeshttps://doi.org/10.1016/j.jpi.2023.100347openalex-snapshot
Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities
inferred← citeshttps://doi.org/10.1016/j.jpi.2023.100357openalex-snapshot
Computational pathology: A survey review and the way forward
inferred← citeshttps://doi.org/10.1016/j.jpi.2024.100386openalex-snapshot
Masked pre-training of transformers for histology image analysis
inferred← citeshttps://doi.org/10.1016/j.jpi.2024.100413openalex-snapshot
Attention induction based on pathologist annotations for improving whole slide pathology image classifier
inferred← citeshttps://doi.org/10.1016/j.jpi.2024.100418openalex-snapshot
“Pathologists in Venice – Real World Cases for an Immersive Training Experience”: Education, Gaming, and Show
inferred← citeshttps://doi.org/10.1016/j.jpi.2025.100420openalex-snapshot
Assessing the quality of whole slide images in cytology from nuclei features
inferred← citeshttps://doi.org/10.1016/j.jpi.2025.100462openalex-snapshot
Automatic labels are as effective as manual labels in digital pathology images classification with deep learning
inferred← citeshttps://doi.org/10.1016/j.jpi.2025.100531openalex-snapshot
Digital pathology imaging artificial intelligence in cancer research and clinical trials: An NCI workshop report
inferred← citeshttps://doi.org/10.1016/j.jpi.2025.100537openalex-snapshot
ADPv2: A hierarchical histological tissue type-annotated dataset for potential biomarker discovery of colorectal disease
inferred← citeshttps://doi.org/10.1016/j.jpi.2025.100542openalex-snapshot
Applications and challenges of utilizing digital pathology and AI-enabled workflows in clinical trials
inferred← citeshttps://doi.org/10.1016/j.jpi.2026.100656openalex-snapshot
Detection of macrovesicular steatosis in hematoxylin and eosin-stained histological images of human livers: A feature-based method
inferred← citeshttps://doi.org/10.1016/j.jpi.2026.100674openalex-snapshot
REMIL-IBD: Region-filtered multiple instance learning for interpretable slide-level grading of inflammatory bowel disease
inferred← citeshttps://doi.org/10.1016/j.jpi.2026.100681openalex-snapshot
Regulatory science for AI-based software as a medical device in computational pathology and biomarker-driven drug development
inferred← citeshttps://doi.org/10.1016/j.jpi.2026.100747openalex-snapshot
MyoPath: A deep learning pipeline for objective morphometric assessment of skeletal muscle biopsies
inferred← citeshttps://doi.org/10.1016/j.kint.2025.06.029openalex-snapshot
Advances in computational nephropathology
inferred← citeshttps://doi.org/10.1016/j.labinv.2023.100255openalex-snapshot
Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models
inferred← citeshttps://doi.org/10.1016/j.labinv.2023.100261openalex-snapshot
Real-World Implementation of Digital Pathology: Results From an Intercontinental Survey
inferred← citeshttps://doi.org/10.1016/j.labinv.2024.102111openalex-snapshot
Implementation of Digital Pathology and Artificial Intelligence in Routine Pathology Practice
inferred← citeshttps://doi.org/10.1016/j.labinv.2025.104178openalex-snapshot
Computer-Based Detection of Colorectal Serrated Lesions: Digital Flatness, a Novel Metric Designed for Whole-Slide Images
inferred← citeshttps://doi.org/10.1016/j.mcpdig.2025.100302openalex-snapshot
Unbiased Artificial Intelligence: Addressing Bias in Computational Pathology
inferred← citeshttps://doi.org/10.1016/j.measen.2024.101646openalex-snapshot
Trustworthy, self-fulfilling, non-redundant proficiency tests for metrological challenges in medical diagnostics
inferred← citeshttps://doi.org/10.1016/j.media.2022.102539openalex-snapshot
CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&E-stained Histopathological Images
inferred← citeshttps://doi.org/10.1016/j.media.2022.102594openalex-snapshot
A framework for falsifiable explanations of machine learning models with an application in computational pathology
inferred← citeshttps://doi.org/10.1016/j.media.2023.102743openalex-snapshot
Handcrafted Histological Transformer (H2T): Unsupervised representation of whole slide images
inferred← citeshttps://doi.org/10.1016/j.media.2023.102748openalex-snapshot
Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images
inferred← citeshttps://doi.org/10.1016/j.media.2023.102885openalex-snapshot
An aggregation of aggregation methods in computational pathology
inferred← citeshttps://doi.org/10.1016/j.media.2024.103191openalex-snapshot
A systematic comparison of deep learning methods for Gleason grading and scoring
inferred← citeshttps://doi.org/10.1016/j.media.2024.103252openalex-snapshot
Dual-stream multi-dependency graph neural network enables precise cancer survival analysis
inferred← citeshttps://doi.org/10.1016/j.media.2024.103303openalex-snapshot
Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning
inferred← citeshttps://doi.org/10.1016/j.micron.2024.103663openalex-snapshot
A study of criteria for grading follicular lymphoma using a cell type classifier from pathology images based on complementary-label learning
inferred← citeshttps://doi.org/10.1016/j.mimet.2025.107131openalex-snapshot
Using pathology images and artificial intelligence to identify bacterial infections and their types
inferred← citeshttps://doi.org/10.1016/j.mimet.2025.107180openalex-snapshot
Identification of bacterial infection types in decomposition stages at various temperatures using pathology images and artificial intelligence algorithms
inferred← citeshttps://doi.org/10.1016/j.mlwa.2024.100579openalex-snapshot
Detection of presence or absence of metastasis in WSI patches of breast cancer using the dual-enhanced convolutional ensemble neural network
inferred← citeshttps://doi.org/10.1016/j.modpat.2022.100088openalex-snapshot
MarrowQuant 2.0: A Digital Pathology Workflow Assisting Bone Marrow Evaluation in Experimental and Clinical Hematology
inferred← citeshttps://doi.org/10.1016/j.modpat.2023.100254openalex-snapshot
Deciphering the Morphology of Tumor-Stromal Features in Invasive Breast Cancer Using Artificial Intelligence
inferred← citeshttps://doi.org/10.1016/j.mpdhp.2020.07.002openalex-snapshot
Multiplex immunohistochemistry in lymphoma pathology: a research tool for study of the immune microenvironment
inferred← citeshttps://doi.org/10.1016/j.nbt.2023.09.008openalex-snapshot
Shedding light on the black box of a neural network used to detect prostate cancer in whole slide images by occlusion-based explainability
inferred← citeshttps://doi.org/10.1016/j.path.2023.11.013openalex-snapshot
Applications of Artificial Intelligence in Lung Pathology
inferred← citeshttps://doi.org/10.1016/j.pbiomolbio.2022.11.004openalex-snapshot
Exploring different computational approaches for effective diagnosis of breast cancer
inferred← citeshttps://doi.org/10.1016/j.prp.2024.155539openalex-snapshot
Multidisciplinary Teams in Musculoskeletal Infection – From a Pathologist’s Perspective
inferred← citeshttps://doi.org/10.1016/j.rcreu.2021.04.001openalex-snapshot
Understanding tubulointerstitial injury and repair mechanisms paves the way for renal outcome improvement in lupus nephritis
inferred← citeshttps://doi.org/10.1016/j.semcancer.2023.09.006openalex-snapshot
One label is all you need: Interpretable AI-enhanced histopathology for oncology
inferred← citeshttps://doi.org/10.1016/j.tvjl.2026.106878openalex-snapshot
A deep learning framework for histopathological classification of canine testicular tumours
inferred← citeshttps://doi.org/10.1016/j.urolonc.2022.03.003openalex-snapshot
Artificial intelligence in prostate cancer: Definitions, current research, and future directions
inferred← citeshttps://doi.org/10.1016/j.xcrm.2026.103054openalex-snapshot
Class visualizations and activation atlases for computational pathology
inferred← citeshttps://doi.org/10.1038/s41374-020-00514-0openalex-snapshot
Artificial intelligence and computational pathology
inferred← citeshttps://doi.org/10.1038/s41374-021-00537-1openalex-snapshot
Quantitative analysis of abnormalities in gynecologic cytopathology with deep learning
inferred← citeshttps://doi.org/10.1038/s41377-021-00674-8openalex-snapshot
Biopsy-free in vivo virtual histology of skin using deep learning
inferred← citeshttps://doi.org/10.1038/s41377-026-02347-wopenalex-snapshot
Universal and transferable attacks on pathology foundation models using microscopic perturbations
inferred← citeshttps://doi.org/10.1038/s41379-021-00919-2openalex-snapshot
Digital pathology and artificial intelligence in translational medicine and clinical practice
inferred← citeshttps://doi.org/10.1038/s41379-021-01000-8openalex-snapshot
Potential quality pitfalls of digitalized whole slide image of breast pathology in routine practice
inferred← citeshttps://doi.org/10.1038/s41379-022-01147-yopenalex-snapshot
Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology
inferred← citeshttps://doi.org/10.1038/s41388-023-02797-1openalex-snapshot
Pathologists’ first opinions on barriers and facilitators of computational pathology adoption in oncological pathology: an international study
inferred← citeshttps://doi.org/10.1038/s41416-022-01986-1openalex-snapshot
Role of AI and digital pathology for colorectal immuno-oncology
inferred← citeshttps://doi.org/10.1038/s41467-023-37224-2openalex-snapshot
Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology
inferred← citeshttps://doi.org/10.1038/s41467-025-66889-0openalex-snapshot
Adaptive compression framework for giga-pixel whole slide images
inferred← citeshttps://doi.org/10.1038/s41523-024-00663-1openalex-snapshot
A panoptic segmentation dataset and deep-learning approach for explainable scoring of tumor-infiltrating lymphocytes
inferred← citeshttps://doi.org/10.1038/s41540-023-00301-9openalex-snapshot
Extending the landscape of omics technologies by pathomics
inferred← citeshttps://doi.org/10.1038/s41551-019-0472-6openalex-snapshot
No pixel-level annotations needed
inferred← citeshttps://doi.org/10.1038/s41551-021-00789-8openalex-snapshot
Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data
inferred← citeshttps://doi.org/10.1038/s41581-020-0321-6openalex-snapshot
Digital pathology and computational image analysis in nephropathology
inferred← citeshttps://doi.org/10.1038/s41581-026-01102-zopenalex-snapshot
Next-generation kidney tissue analysis — spatial omics and digital pathology
inferred← citeshttps://doi.org/10.1038/s41591-021-01343-4openalex-snapshot
Deep learning in histopathology: the path to the clinic
inferred← citeshttps://doi.org/10.1038/s41591-023-02643-7openalex-snapshot
A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer
inferred← citeshttps://doi.org/10.1038/s41591-024-03141-0openalex-snapshot
A foundation model for clinical-grade computational pathology and rare cancers detection
inferred← citeshttps://doi.org/10.1038/s41592-018-0069-0openalex-snapshot
Quanti.us: a tool for rapid, flexible, crowd-based annotation of images
inferred← citeshttps://doi.org/10.1038/s41598-021-93746-zopenalex-snapshot
CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance
inferred← citeshttps://doi.org/10.1038/s41598-021-94544-3openalex-snapshot
The value of GATA6 immunohistochemistry and computer-assisted diagnosis to predict clinical outcome in advanced pancreatic cancer
inferred← citeshttps://doi.org/10.1038/s41598-022-08351-5openalex-snapshot
Automated quality assessment of large digitised histology cohorts by artificial intelligence
inferred← citeshttps://doi.org/10.1038/s41598-023-44897-8openalex-snapshot
Application of multiple-finding segmentation utilizing Mask R-CNN-based deep learning in a rat model of drug-induced liver injury
inferred← citeshttps://doi.org/10.1038/s41598-024-54489-9openalex-snapshot
Creating an atlas of normal tissue for pruning WSI patching through anomaly detection
inferred← citeshttps://doi.org/10.1038/s41598-024-56867-9openalex-snapshot
A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma
inferred← citeshttps://doi.org/10.1038/s41598-024-78987-yopenalex-snapshot
Exploratory Analysis of Radiomics and Pathomics in Uterine Corpus Endometrial Carcinoma
inferred← citeshttps://doi.org/10.1038/s41598-025-13546-7openalex-snapshot
An open-source platform for structured annotation and computational workflows in digital pathology research
inferred← citeshttps://doi.org/10.1038/s41598-025-21787-9openalex-snapshot
Detection, localization, and staging of breast cancer lymph node metastasis in digital pathology whole slide images using selective neighborhood attention-based deep learning
inferred← citeshttps://doi.org/10.1038/s41598-026-37455-5openalex-snapshot
AI caption generation model for digital pathology of adenocarcinoma in endoscopic histopathology using multi-instance attention mechanisms
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