ReviewThe American journal of pathology2019
Pathology Image Analysis Using Segmentation Deep Learning Algorithms.
Review in The American journal of pathology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 160 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
160 citing papers in PubMed, 3 syntheses or guidelines pooled it, 372 citations in OpenAlex.
- Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis.Pediatric surgery international · 2026Pooled it
- The role of artificial intelligence in predicting the clinical outcomes associated with different therapeutic approaches for vestibular schwannoma: A systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Pooled it
- The Role of Deep Learning-Based Echocardiography in the Diagnosis and Evaluation of the Effects of Routine Anti-Heart-Failure Western Medicines in Elderly Patients with Acute Left Heart Failure.Journal of healthcare engineering · 2021Trial
- Deep learning-driven 3-D histopathology method: a pipeline for cellular-resolution myocarditis analysis.American journal of physiology. Heart and circulatory physiology · 2026Article
- 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
- A deep learning framework for histopathological analysis of pixel-level extracellular matrix variation in standard H&E-stained images.Scientific reports · 2026Article
- Multimodal fusion-based prediction of postoperative survival in gallbladder adenocarcinoma: Model development and validation.Scientific reports · 2026Article
- MicNet: integrating spatially resolved transcriptomes and pathology images by contrastive deep neural network.Genome biology · 2026Article
- Comparison of Deep Learning Tools for Optic Nerve Axon Quantification Finds Limited Generalizability Upon Independent Validation.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Deep neural networks in medical microbiology for bacterial colonies classification.Scientific reports · 2026Article
- Deep learning-based multimodal radiopathomics for preoperative prediction of lymph node metastasis in papillary thyroid carcinoma.Scientific reports · 2026Article
- Establishment of a Model to Predict the Prognosis of Endometrial Carcinoma Using Tumor-Infiltrating Lymphocytes Evaluated With Artificial Intelligence: A Retrospective Analysis.Cancer reports (Hoboken, N.J.) · 2026Article
- Computational pathology with dynamic convolutional and adaptive kernels.Journal of pathology informatics · 2026Article
- Comparison of Deep Learning Tools for Optic Nerve Axon Quantification Finds Limited Generalizability on Independent Validation.bioRxiv : the preprint server for biology · 2026Article
- Evaluating deep learning models for pancreatic cancer diagnosis.Clinical and experimental medicine · 2026Article
- Medical students perceptions and attitudes toward the use of generative artificial intelligence in clinical decision-making: a nationwide cross-sectional survey in China.BMC medical education · 2026Article
- PathViT Model for Automated Disease Classification from Skeletal Muscle Histopathology.The American journal of pathology · 2026Article
- Perineural invasion in solid tumors: biological foundations and the emerging integration of machine learning and artificial intelligence.Frontiers in oncology · 2026Review
100 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
5 authors at 2 institutions in 1 country.
Funding
Abstract
With the rapid development of image scanning techniques and visualization software, whole slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis from pathology images and automating image analysis efficiently and accurately remain significant challenges. Recently, deep learning algorithms have shown great promise in pathology image analysis, such as in tumor region identification, metastasis detection, and patient prognosis. Many machine learning algorithms, including convolutional neural networks, have been proposed to automatically segment pathology images. Among these algorithms, segmentation deep learning algorithms such as fully convolutional networks stand out for their accuracy, computational efficiency, and generalizability. Thus, deep learning-based pathology image segmentation has become an important tool in WSI analysis. In this review, the pathology image segmentation process using deep learning algorithms is described in detail. The goals are to provide quick guidance for implementing deep learning into pathology image analysis and to provide some potential ways of further improving segmentation performance. Although there have been previous reviews on using machine learning methods in digital pathology image analysis, this is the first in-depth review of the applications of deep learning algorithms for segmentation in WSI analysis.
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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.