ReviewComputational and structural biotechnology journal2018
Machine Learning Methods for Histopathological Image Analysis.
Review in Computational and structural biotechnology journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 284 papers, 3 of them syntheses that pooled it.
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
284 citing papers in PubMed, 3 syntheses or guidelines pooled it, 886 citations in OpenAlex.
- Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Virtual histopathology methods in medical imaging - a systematic review.BMC medical imaging · 2024Pooled it
- Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis.BMC oral health · 2024Pooled it
- Opportunities and challenges of artificial intelligence and digital pathology in adult CNS tumors.Journal of neuro-oncology · 2026Review
- Reclaiming Anatomy as Method: From Morphological Reasoning to Clinical Relevance.Clinical anatomy (New York, N.Y.) · 2026Review
- A Hybrid Swin Transformer and Texture Feature Framework for Histopathological Classification of Paratuberculosis.Bioengineering (Basel, Switzerland) · 2026Article
- Neighbor-constrained attention for multiple instance learning in whole-slide histopathology.Medical & biological engineering & computing · 2026Article
- V7-DiVA: a feature-based Deep MIL colorectal whole-slide histopathology research platform with blinded external image-input validation.Journal of translational medicine · 2026Article
- TransBreast-Net: An interpretable vision transformer ensemble for breast cancer histopathology classification.Journal of pathology informatics · 2026Article
- Combining residual U-Net and data augmentation for dense temporal segmentation of spike wave discharges in single-channel EEG.Scientific reports · 2026Article
- Automated Eosinophil Quantification Using Deep Learning to Predict Therapy Escalation in Pediatric Ulcerative Colitis.Clinical and translational gastroenterology · 2026Article
- Machine Learning for Colitis-Associated Cancer in Inflammatory Bowel Disease: Evidence and Future Directions Toward Precision Medicine, a Narrative Review.International journal of molecular sciences · 2026Review
- CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training.Neuroinformatics · 2026Article
- Development and validation of pathomics signature for predicting prognosis of advanced high-grade serous ovarian carcinoma patients after platinum-based chemotherapy.Scientific reports · 2026Article
- TransNet-SAM2: A Transformer-Foundation Model Framework for Prompt-Free Segmentation of White Blood Cells in Microscopic Blood Smear Images.Diagnostics (Basel, Switzerland) · 2026Article
- Continual Learning for Histopathology Image Classification in Class-Incremental Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Use of Artificial Intelligence in the Early Diagnosis of Oral Cancer: Current Update, Limitations and Future Perspectives.Journal of dentistry (Shiraz, Iran) · 2026Article
- Comparison of Deep Learning Tools for Optic Nerve Axon Quantification Finds Limited Generalizability Upon Independent Validation.Bioengineering (Basel, Switzerland) · 2026Article
- Ligand-Receptor Interaction Combined with Histopathology Improves Glioma Prognostic Model.Biomedicines · 2026Article
- Performance and generalization analysis of machine learning, deep learning, and transformer models for histopathology image classification.Scientific reports · 2026Article
224 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
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.
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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.