ReviewMedical image analysis2021
Deep neural network models for computational histopathology: A survey.
Review in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 210 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
210 citing papers in PubMed, 695 citations in OpenAlex.
- HistoEncoder: A digital pathology foundation model for prostate cancer.Journal of pathology informatics · 2026Article
- A new era in computational pathology: A survey on foundation and vision-language models.Journal of pathology informatics · 2026Review
- Generalizability of Deep Learning Architectures for Oral Squamous Cell Carcinoma Classification in Histopathology: A Comparative CNN-Vision Transformer Study with External Validation and Label-Quality Analysis.Journal of imaging informatics in medicine · 2026Article
- Foundation model-powered deep learning of endometrial histology for predicting the cumulative live birth of an in vitro fertilization cycle.PLOS digital health · 2026Article
- ConMIL: interactive and contrastive text-guided multiple instance learning for whole slide image classification.Bioinformatics (Oxford, England) · 2026Article
- Artificial Intelligence for Diagnostic and Prognostic Support in Breast Cancer: A Literature Overview.Cancers · 2026Review
- Computational Pathology Reveals Extracellular-Matrix Imaging Biomarkers of Therapy Response in Preclinical Breast Cancer.Cancers · 2026Article
- Domain-Specific Transfer Learning for Gastric Cancer Tissue Classification.Journal of imaging informatics in medicine · 2026Article
- Article
- Cross-Modality Alignment of Spatial Transcriptomics, Multiplexed Imaging, and Histology with PHARAOH.Research square · 2026Article
- A comparative analysis of deep learning models for disease classification in multi-organ histopathological images.Scientific reports · 2026Article
- Comparison of Deep Learning Tools for Optic Nerve Axon Quantification Finds Limited Generalizability Upon Independent Validation.Bioengineering (Basel, Switzerland) · 2026Article
- Comparative Evaluation of Feature Extractors, Aggregation Strategies, and Classification Hierarchies for Ovarian Cancer Subtype Classification in Whole Slide Images.Diagnostics (Basel, Switzerland) · 2026Article
- Performance and generalization analysis of machine learning, deep learning, and transformer models for histopathology image classification.Scientific reports · 2026Article
- Comprehensive evaluation of cross cancer generalization in histopathology segmentation models across 21 tumor types.Communications medicine · 2026Article
- PathoTiroid dataset: Indonesian collection (PTIC)-a histopathology image dataset for papillary thyroid carcinoma.BMC research notes · 2026Article
- Risk Classification of Low-Resolution Whole-Slide Thumbnail Images by Multi-dimensional Feature Reconstruction with Multi-task Deep Learning Network Helps Prioritize Pathology Case Registration.Journal of imaging informatics in medicine · 2026Article
- A dual-consistency semi-supervised learning method for histopathology image segmentation.BMC medical imaging · 2026Article
- Comparison of Deep Learning Tools for Optic Nerve Axon Quantification Finds Limited Generalizability on Independent Validation.bioRxiv : the preprint server for biology · 2026Article
- AI caption generation model for digital pathology of adenocarcinoma in endoscopic histopathology using multi-instance attention mechanisms.Scientific reports · 2026Article
150 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to disease progression and patient survival outcomes. Recently, deep learning has become the mainstream methodological choice for analyzing and interpreting histology images. In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis. From the survey of over 130 papers, we review the field's progress based on the methodological aspect of different machine learning strategies such as supervised, weakly supervised, unsupervised, transfer learning and various other sub-variants of these methods. We also provide an overview of deep learning based survival models that are applicable for disease-specific prognosis tasks. Finally, we summarize several existing open datasets and highlight critical challenges and limitations with current deep learning approaches, along with possible avenues for future research.
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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.