ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2018
Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology.
Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 58 citations in OpenAlex.
- Deployment of AI-driven automated quality control of whole-slide images in a large tertiary cancer center.Journal of pathology informatics · 2026Article
- Digital neuropathology of neurodegenerative disorders: Foundations, research advances, and future directions.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Assessing the quality of whole slide images in cytology from nuclei features.Journal of pathology informatics · 2025Article
- Study on the Transformation Process of Thyroid Fine-Needle Aspiration Liquid-Based Cytology to Whole-Slide Image.Cytopathology : official journal of the British Society for Clinical Cytology · 2025Article
- Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo.Biosensors & bioelectronics · 2025Review
- GrandQC: A comprehensive solution to quality control problem in digital pathology.Nature communications · 2024Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 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
- [Artificial intelligence for pathology-how, where, and why?]Pathologie (Heidelberg, Germany) · 2024Article
- Artificial intelligence in pathologic diagnosis, prognosis and prediction of prostate cancer.American journal of clinical and experimental urology · 2024Review
- Synergies and Challenges in the Preclinical and Clinical Implementation of Pathology Artificial Intelligence Applications.Mayo Clinic proceedings. Digital health · 2023Review
- Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance.Diagnostics (Basel, Switzerland) · 2023Review
- Automated quality assessment of large digitised histology cohorts by artificial intelligence.Scientific reports · 2022Article
- How to learn with intentional mistakes: NoisyEnsembles to overcome poor tissue quality for deep learning in computational pathology.Frontiers in medicine · 2022Article
- Quality control stress test for deep learning-based diagnostic model in digital pathology.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2021Article
- Multi-Task Learning-Based Immunofluorescence Classification of Kidney Disease.International journal of environmental research and public health · 2021Article
- Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Out-of-focus brain image detection in serial tissue sections.Journal of neuroscience methods · 2020Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Pathology is on the verge of a profound change from an analog and qualitative to a digital and quantitative discipline. This change is mostly driven by the high-throughput scanning of microscope slides in modern pathology departments, reaching tens of thousands of digital slides per month. The resulting vast digital archives form the basis of clinical use in digital pathology and allow large scale machine learning in computational pathology. One of the most crucial bottlenecks of high-throughput scanning is quality control (QC). Currently, digital slides are screened manually to detected out-of-focus regions, to compensate for the limitations of scanner software. We present a solution to this problem by introducing a benchmark dataset for blur detection, an in-depth comparison of state-of-the art sharpness descriptors and their prediction performance within a random forest framework. Furthermore, we show that convolution neural networks, like residual networks, can be used to train blur detectors from scratch. We thoroughly evaluate the accuracy of feature based and deep learning based approaches for sharpness classification (99.74% accuracy) and regression (MSE 0.004) and additionally compare them to domain experts in a comprehensive human perception study. Our pipeline outputs spacial heatmaps enabling to quantify and localize blurred areas on a slide. Finally, we tested the proposed framework in the clinical setting and demonstrate superior performance over the state-of-the-art QC pipeline comprising commercial software and human expert inspection by reducing the error rate from 17% to 4.7%.
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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.