ReviewJournal of clinical and translational science2020
Developing image analysis pipelines of whole-slide images: Pre- and post-processing.
Review in Journal of clinical and translational science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue.Cancers · 2025Review
- Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions.Mayo Clinic proceedings. Digital health · 2025Review
- Deep Gaussian process with uncertainty estimation for microsatellite instability and immunotherapy response prediction from histology.NPJ digital medicine · 2025Article
- Assessing the quality of whole slide images in cytology from nuclei features.Journal of pathology informatics · 2025Article
- High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma.Scientific data · 2024Article
- Considerations on Image Preprocessing Techniques Required by Deep Learning Models. The Case of the Knee MRIs.Maedica · 2024Article
- Histopathology imaging and clinical data including remission status in pediatric inflammatory bowel disease.Scientific data · 2024Article
- Demographic bias in misdiagnosis by computational pathology models.Nature medicine · 2024Article
- Semi-automated analysis of HER2 immunohistochemistry in invasive breast carcinoma using whole slide images: utility for interpretation in clinical practice.Pathology oncology research : POR · 2024Article
- Building Automation Pipeline for Diagnostic Classification of Sporadic Odontogenic Keratocysts and Non-Keratocysts Using Whole-Slide Images.Diagnostics (Basel, Switzerland) · 2023Article
- AI in Pathology: What could possibly go wrong?Seminars in diagnostic pathology · 2023Review
- Whole Slide Images in Artificial Intelligence Applications in Digital Pathology: Challenges and Pitfalls.Turk patoloji dergisi · 2023Article
- Cell2Grid: an efficient, spatial, and convolutional neural network-ready representation of cell segmentation data.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Artificial intelligence as a tool for diagnosis in digital pathology whole slide images: A systematic review.Journal of pathology informatics · 2022Review
- Squamous Cell Carcinoma of Skin Cancer Margin Classification From Digital Histopathology Images Using Deep Learning.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning has pushed the scope of digital pathology beyond simple digitization and telemedicine. The incorporation of these algorithms in routine workflow is on the horizon and maybe a disruptive technology, reducing processing time, and increasing detection of anomalies. While the newest computational methods enjoy much of the press, incorporating deep learning into standard laboratory workflow requires many more steps than simply training and testing a model. Image analysis using deep learning methods often requires substantial pre- and post-processing order to improve interpretation and prediction. Similar to any data processing pipeline, images must be prepared for modeling and the resultant predictions need further processing for interpretation. Examples include artifact detection, color normalization, image subsampling or tiling, removal of errant predictions, etc. Once processed, predictions are complicated by image file size - typically several gigabytes when unpacked. This forces images to be tiled, meaning that a series of subsamples from the whole-slide image (WSI) are used in modeling. Herein, we review many of these methods as they pertain to the analysis of biopsy slides and discuss the multitude of unique issues that are part of the analysis of very large images.
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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.