ReviewIEEE reviews in biomedical engineering2009
Histopathological image analysis: a review.
Review in IEEE reviews in biomedical engineering, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 548 papers, 2 of them syntheses that pooled it.
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.
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
548 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis.BMC oral health · 2024Pooled it
- Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.Seminars in cancer biology · 2022Pooled it
- Image-based analysis of lymphoma cell heterogeneity: Comparative study of CRYO and FFPE techniques.Journal of pathology informatics · 2026Article
- ShadoNet: a cell detection and classification framework for Ki-67 pathology images.Bioinformatics (Oxford, England) · 2026Article
- Autoencoders for unsupervised analysis of rat myeloarchitecture.Brain structure & function · 2026Article
- Digital Quantification of Membrane DAB Immunohistochemical Staining in FFPE Cervical Cancer Tissues Using an Open-Source CellProfiler Pipeline.Bio-protocol · 2026Article
- A Hybrid Swin Transformer and Texture Feature Framework for Histopathological Classification of Paratuberculosis.Bioengineering (Basel, Switzerland) · 2026Article
- MeCaMIL: Causality-Aware Multiple Instance Learning for Fair and Interpretable Whole Slide Image Diagnosis.IEEE transactions on medical imaging · 2026Article
- Pathology-CoT: learning visual chain-of-thought agents from expert whole-slide image diagnosis behaviour.Nature biomedical engineering · 2026Article
- Microscopy Cell Segmentation: Review and Benchmarking of Task-Specific and Foundation Models.Journal of imaging · 2026Review
- Bridging traditional and deep learning methods in H&E histological image normalization: a comprehensive review and introducing a novel framework for comparative analyses.Journal of advanced research · 2026Review
- Functional DNAzyme-Guided Target Identification and Portable Detection of Colorectal Cancer.JACS Au · 2026Article
- LRF-CNN: An explainable lightweight receptive field-based CNN for colorectal cancer histopathological image classification.iScience · 2026Article
- Cell shapes decode molecular phenotypes in image-based spatial proteomics.Cell systems · 2026Article
- Transcription Factor TBX5 Enhances Structural Complexity and Calcium Transient Synchronization in Spontaneously Active Neonatal Rat Cardiomyocyte Cultures.Doklady biological sciences : proceedings of the Academy of Sciences of the USSR, Biological sciences sections · 2026Article
- Virtual immunohistochemistry by conditional generative adversarial networks.Scientific reports · 2026Article
- Diffusion attention expert model for predicting and semi-automatic localizing STAS in lung cancer histopathological images.Nature communications · 2026Article
- Comparative Analysis of Deep Learning Approaches for Histopathology-Based Survival Prediction in Hepatocellular Carcinoma.Cancers · 2026Article
- Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets.Medical image analysis · 2026Article
- Patient-Specific Constitutive Models Based on Biaxial and Microstructural Characterisation of Fresh Human Gastric Tissue.Annals of biomedical engineering · 2026Article
488 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
6 authors.
Funding
Abstract
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.
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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.