ReviewJournal of pathology informatics2025
Deep learning for digital pathology: A critical overview of methodological framework.
Review in Journal of pathology informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Swarm intelligence-guided ROI selection for deep learning assessment of HER2 in colorectal cancer.Scientific reports · 2026Article
- Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.Briefings in bioinformatics · 2026Review
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Integration of digital slide-based self-directed learning with the post-class video-recording assignments into oral pathology and diagnosis course can improve dental students' learning outcomes and microscopic diagnosis capability.Journal of dental sciences · 2026Article
- Comparative Analysis of Pathology Foundation Models for Automated Detection of Tertiary Lymphoid Structures in Hematoxylin-and-Eosin-Stained Digital Pathology Images.Computational and structural biotechnology journal · 2026Article
- Quantitative Assessment of Focus Quality in Whole-Slide Imaging of Thyroid Liquid-Based Cytology Using Laplacian Variance.Endocrine pathology · 2025Article
- Comprehensive benchmarking of deep learning architectures for multiclass histopathological classification of oral epithelial lesions.Journal of oral biology and craniofacial researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning frameworks have transformed the field of digital pathology by automating complex tasks and revealing intricate patterns within histopathological data. These advanced methodologies provide exceptional accuracy and scalability, facilitating the analysis of high-dimensional whole-slide images with unparalleled precision. In this article, we present a comprehensive deep learning framework highlighting recent advancements in computational pathology. We critically examine mathematical innovations and offer a comparative analysis of various models demonstrating the significant and ongoing improvements in the field.
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What OpenQuestion holds
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.