ReviewCancers2022
Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
44 citing papers in PubMed, 117 citations in OpenAlex.
- Autoencoders for unsupervised analysis of rat myeloarchitecture.Brain structure & function · 2026Article
- A Hybrid Swin Transformer and Texture Feature Framework for Histopathological Classification of Paratuberculosis.Bioengineering (Basel, Switzerland) · 2026Article
- Foundation artificial intelligence models enable high-accuracy diagnostic differentiation of hybrid neurofibroma/schwannoma using whole-slide images.Journal of pathology informatics · 2026Article
- Histopathological Assessment of Myocardial Ischemia-Reperfusion Injury Using Transformer-Based Artificial Intelligence: Model Comparison Study.JMIR medical informatics · 2026Article
- PathViT Model for Automated Disease Classification from Skeletal Muscle Histopathology.The American journal of pathology · 2026Article
- Machine learning-based integration of tumor deposit molecular signatures improves prognostic stratification in colon adenocarcinoma.International journal of colorectal disease · 2026Article
- Enhanced colorectal gland segmentation through multi-scale attention and contextual feature fusion.Scientific reports · 2026Article
- HiGATE: hierarchical graph attention for multi-scale tissue encoder in computational pathology.Frontiers in oncology · 2026Article
- Federated Learning for Histopathology Image Classification: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- AI-assisted quantitative analysis for evaluating melanin distribution in 3D pigmented epidermis-on-a-chip models.Frontiers in bioengineering and biotechnology · 2026Article
- A Lightweight Cross-Gated Dual-Branch Attention Network for Colon and Lung Cancer Diagnosis from Histopathological Images.Medical sciences (Basel, Switzerland) · 2025Article
- A Robust Framework for Domain-Generalized Classification of Ovarian Cancer Histology Images.Diagnostics (Basel, Switzerland) · 2025Article
- Cross-platform multi-cancer histopathology classification using local-window vision transformers.Scientific reports · 2025Article
- Deep learning for digital pathology: A critical overview of methodological framework.Journal of pathology informatics · 2025Review
- Unbiased self supervised learning of kidney histology reveals phenotypic and prognostic insights.Scientific reports · 2025Article
- Chemically-induced histopathological progression in the polycystic ovary syndrome (PCOS)- A review.Toxicological research · 2025Review
- Deep learning-based differentiation of benign and malignant thyroid follicular neoplasms on multiscale intraoperative frozen pathological images: A multicenter diagnostic study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Deep learning in histopathology images for prediction of oncogenic driver molecular alterations in lung cancer: a systematic review and meta-analysis.Translational lung cancer research · 2025Article
- Advanced Deep Learning Approaches in Detection Technologies for Comprehensive Breast Cancer Assessment Based on WSIs: A Systematic Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Introducing the contextual digital divide: Insights from microscopic anatomy on usage behavior and effectiveness of digital versus face-to-face learning.Anatomical sciences education · 2025Article
Corrections and comments
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Authors and funding
14 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.