ReviewFrontiers of medicine2020
Deep learning in digital pathology image analysis: a survey.
Review in Frontiers of medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 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
59 citing papers in PubMed.
- A Transformer-Residual Network-Integrated Deep Learning Model for Digital Pathology-Based Grading and Risk Stratification of Metabolic Dysfunction-Associated Steatotic Liver Disease-Related Liver Fibrosis.Clinical and translational gastroenterology · 2026Article
- DyMamba: dynamic Mamba for microscopy image semantic segmentation.Bioinformatics (Oxford, England) · 2026Article
- The role of diffusion-weighted MRI in biological image-guided radiation therapy: a roadmap.Physics in medicine and biology · 2026Review
- Multimodal deep learning framework for recurrence risk stratification in soft tissue sarcoma: a multicenter study.NPJ precision oncology · 2026Article
- A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.Nature biomedical engineering · 2026Article
- Non-invasive quantification of viability in liver spheroids using deep learning.Frontiers in bioengineering and biotechnology · 2026Article
- Deep learning model for predicting extraprostatic extension of prostate cancer based on H&E-stained biopsy digital images.Annals of medicine · 2025Article
- VISGAB: Virtual staining-driven GAN benchmarking for optimizing skin tissue histology.Scientific reports · 2025Article
- Evaluating cell AI foundation models in kidney pathology with human-in-the-loop enrichment.Communications medicine · 2025Article
- Deep learning for digital pathology: A critical overview of methodological framework.Journal of pathology informatics · 2025Review
- An artificial intelligence model of whole-slide pathology specimens differentiating cutaneous high-grade squamous proliferations.Virchows Archiv : an international journal of pathology · 2025Article
- The healing process of vertebral body fracture in Wistar rats: creation of an animal model and demonstration of the impact of anterolateral vascularization disruption on bone healing.Journal of spine surgery (Hong Kong) · 2025Article
- Automatic labels are as effective as manual labels in digital pathology images classification with deep learning.Journal of pathology informatics · 2025Article
- Improving the Annotation Process in Computational Pathology: A Pilot Study with Manual and Semi-automated Approaches on Consumer and Medical Grade Devices.Journal of imaging informatics in medicine · 2025Article
- Diagnostic classification in toxicologic pathology using attention-guided weak supervision and whole slide image features: a pilot study in rat livers.Scientific reports · 2025Article
- Assessment of Cell Nuclei AI Foundation Models in Kidney Pathology.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Out-of-distribution generalization for segmentation of lymph node metastasis in breast cancer.Scientific reports · 2025Article
- Deep learning based analysis of G3BP1 protein expression to predict the prognosis of nasopharyngeal carcinoma.PloS one · 2025Article
- Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.International journal of clinical oncology · 2024Review
- Efficient, gigapixel-scale, aberration-free whole slide scanner using angular ptychographic imaging with closed-form solution.Biomedical optics express · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features. In terms of feature extraction, DL approaches are less labor intensive compared with conventional machine learning methods. In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology, including different tasks (e.g., classification, semantic segmentation, detection, and instance segmentation) and various applications (e.g., stain normalization, cell/gland/region structure analysis). DL methods can provide consistent and accurate outcomes. DL is a promising tool to assist pathologists in clinical diagnosis.
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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.