ArticlePloS one2018
DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis 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
53 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP).Diagnostics (Basel, Switzerland) · 2021Guideline
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- Deployment of AI-driven automated quality control of whole-slide images in a large tertiary cancer center.Journal of pathology informatics · 2026Article
- Multicenter Histology Image Integration and Multiscale Deep Learning Support Machine Learning-Enabled Pediatric Sarcoma Classification.Cancer research · 2026Article
- Reliable classification of polyps based on artificial intelligence: a development and validation study.EClinicalMedicine · 2026Article
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- Deep learning in bone marrow cytomorphology: advances in segmentation, classification, and clinical translation.Medical oncology (Northwood, London, England) · 2025Review
- A mixture of experts (MoE) model to improve AI-based computational pathology prediction performance under variable levels of image blur.BMC medical imaging · 2025Article
- Addressing persistent challenges in digital image analysis of cancer tissue: resources developed from a hackathon.Molecular oncology · 2025Review
- Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions.Life (Basel, Switzerland) · 2025Review
- Artificial intelligence in healthcare (Review).Biomedical reports · 2025Review
- GrandQC: A comprehensive solution to quality control problem in digital pathology.Nature communications · 2024Article
- Models for the marrow: A comprehensive review of AI-based cell classification methods and malignancy detection in bone marrow aspirate smears.HemaSphere · 2024Review
- Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs.BMC medical informatics and decision making · 2024Article
- Improving quality control of whole slide images by explicit artifact augmentation.Scientific reports · 2024Article
- Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of 'PathProfiler' in a Diagnostic Pathology Setting.Diagnostics (Basel, Switzerland) · 2024Article
- Slideflow: deep learning for digital histopathology with real-time whole-slide visualization.BMC bioinformatics · 2024Article
- AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer.Scientific reports · 2024Article
- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS).Journal of pathology informatics · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The development of whole slide scanners has revolutionized the field of digital pathology. Unfortunately, whole slide scanners often produce images with out-of-focus/blurry areas that limit the amount of tissue available for a pathologist to make accurate diagnosis/prognosis. Moreover, these artifacts hamper the performance of computerized image analysis systems. These areas are typically identified by visual inspection, which leads to a subjective evaluation causing high intra- and inter-observer variability. Moreover, this process is both tedious, and time-consuming. The aim of this study is to develop a deep learning based software called, DeepFocus, which can automatically detect and segment blurry areas in digital whole slide images to address these problems. DeepFocus is built on TensorFlow, an open source library that exploits data flow graphs for efficient numerical computation. DeepFocus was trained by using 16 different H&E and IHC-stained slides that were systematically scanned on nine different focal planes, generating 216,000 samples with varying amounts of blurriness. When trained and tested on two independent datasets, DeepFocus resulted in an average accuracy of 93.2% (± 9.6%), which is a 23.8% improvement over an existing method. DeepFocus has the potential to be integrated with whole slide scanners to automatically re-scan problematic areas, hence improving the overall image quality for pathologists and image analysis algorithms.
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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.