ReviewJournal of imaging2024
Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives.
Review in Journal of imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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.
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.
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Who cites it
21 citing papers in PubMed.
- Artificial Intelligence-Powered Histopathology in Stem Cell Research: Bridging Morphology, Function, and Omics.Current issues in molecular biology · 2026Review
- A Fixed-Ratio Hybrid ARO-ALO Algorithm for Multi-Level Thresholding of Histopathological Colon Cancer Images.Cancers · 2026Article
- Deep learning single-cell analysis for cytologic evaluation of oral potentially malignant disorders.Scientific reports · 2026Article
- Advances in Avian Diagnostic Pathology: Current Trends, Challenges and Future Directions: A Review.Veterinary medicine and science · 2026Review
- MSWA-ResNet: Multi-Scale Wavelet Attention for Patient-Level and Interpretable Breast Cancer Histopathology Classification.Journal of imaging · 2026Article
- Label-free detection of ovarian cancer cells in ascites-related cell models using digital holographic flow cytometry.Biomedical optics express · 2026Article
- From Hematoxylin and Eosin to Masson's Trichrome: A Comprehensive Framework for Virtual Stain Transformation in Chronic Liver Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2026Article
- Quantitative Texture Analysis of Cervical Cytology Identifies Endometrial Lesions in Atypical Glandular Cells on Liquid-Based Cytology: A Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
- Research on liver cancer pathology image recognition based on deep learning image processing.Scientific reports · 2026Article
- An Adaptive Hybrid Metaheuristic Algorithm for Lung Cancer in Pathological Image Segmentation.Diagnostics (Basel, Switzerland) · 2025Article
- Advancing in vitro cell migration studies: a review of open-source analytical platforms for cancer and wound healing research.Cell adhesion & migration · 2025Review
- Histological Hyperspectral Breast Cancer Recurrence Database (HistologyHSI-BC Recurrence).Scientific data · 2025Article
- A Lightweight Cross-Gated Dual-Branch Attention Network for Colon and Lung Cancer Diagnosis from Histopathological Images.Medical sciences (Basel, Switzerland) · 2025Article
- Next-generation oncology: integrative therapeutic frontiers at the crossroads of precision genomics, immuno-engineering, and tumor microenvironment modulation.Medical oncology (Northwood, London, England) · 2025Review
- From Pixels to Precision-A Dual-Stream Deep Network for Pathological Nuclei Segmentation.Bioengineering (Basel, Switzerland) · 2025Article
- Construction of a Multimodal Machine Learning Model for Papillary Thyroid Carcinoma Based on Pathomics and Ultrasound Radiomics Dataset.Data in brief · 2025Article
- Article
- Advancements in Digital Cytopathology Since COVID-19: Insights from a Narrative Review of Review Articles.Healthcare (Basel, Switzerland) · 2025Review
- Challenges and Opportunities in Cytopathology Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2025Review
- Vision Transformers for Low-Quality Histopathological Images: A Case Study on Squamous Cell Carcinoma Margin Classification.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Both pathology and cytopathology still rely on recognizing microscopical morphologic features, and image analysis plays a crucial role, enabling the identification, categorization, and characterization of different tissue types, cell populations, and disease states within microscopic images. Historically, manual methods have been the primary approach, relying on expert knowledge and experience of pathologists to interpret microscopic tissue samples. Early image analysis methods were often constrained by computational power and the complexity of biological samples. The advent of computers and digital imaging technologies challenged the exclusivity of human eye vision and brain computational skills, transforming the diagnostic process in these fields. The increasing digitization of pathological images has led to the application of more objective and efficient computer-aided analysis techniques. Significant advancements were brought about by the integration of digital pathology, machine learning, and advanced imaging technologies. The continuous progress in machine learning and the increasing availability of digital pathology data offer exciting opportunities for the future. Furthermore, artificial intelligence has revolutionized this field, enabling predictive models that assist in diagnostic decision making. The future of pathology and cytopathology is predicted to be marked by advancements in computer-aided image analysis. The future of image analysis is promising, and the increasing availability of digital pathology data will invariably lead to enhanced diagnostic accuracy and improved prognostic predictions that shape personalized treatment strategies, ultimately leading to better patient outcomes.
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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.