SynthesisBMC medical imaging2024
Virtual histopathology methods in medical imaging - a systematic review.
Synthesis in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Hardware-Attentive Programmable Fourier Ptychography Enables Task-Adaptive Label-Free Virtual Staining.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The Augmented Cytopathologist: A Conceptual Exploratory Narrative Review on Immersive and Vision-Language Models Tools in Digital Pathology.Journal of imaging · 2026Review
- ViT-Stain: Vision transformer-driven virtual staining for skin histopathology via global contextual learning.PloS one · 2026Article
- MLHNet-Lung: an attention-guided multi-level CNN-transformer fusion framework with CBAM and GeM pooling for explainable multiclass lung CT image classification.Frontiers in medicine · 2026Article
- Profiling of the mycobiome and metabolome: a comparative study of benign pulmonary nodules and lung adenocarcinoma.Frontiers in cellular and infection microbiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Virtual histopathology is an emerging technology in medical imaging that utilizes advanced computational methods to analyze tissue images for more precise disease diagnosis. Traditionally, histopathology relies on manual techniques and expertise, often resulting in time-consuming processes and variability in diagnoses. Virtual histopathology offers a more consistent, and automated approach, employing techniques like machine learning, deep learning, and image processing to simulate staining and enhance tissue analysis. This review explores the strengths, limitations, and clinical applications of these methods, highlighting recent advancements in virtual histopathological approaches. In addition, important areas are identified for future research to improve diagnostic accuracy and efficiency in clinical settings.
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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.