ArticleNature communications2024
GrandQC: A comprehensive solution to quality control problem in digital pathology.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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Who cites it
33 citing papers in PubMed.
- CellPrior-net: Prior-guided nuclei detection and classification for H&E whole-slide images.Journal of pathology informatics · 2026Article
- Histological aging signatures for monitoring tissue-specific aging and disease.Nature medicine · 2026Article
- Deep Learning-Assisted Quality Control of Histology Teaching Slides: Detection and Localization of Tissue Fold Artifacts in H&E-Stained Images.Bioengineering (Basel, Switzerland) · 2026Article
- Immune Spatial Organization Predicts Distant Metastasis Risk in Aggressive Localized Prostate Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- GrandQC adaptation as an artificial intelligence tool for quantitative artifact detection in hematoxylin and eosin whole-slide images-Simulation of quality control biopsies day.Journal of pathology informatics · 2026Article
- Benefits of high-magnification and color information in computational pathology-An exploratory review.Journal of pathology informatics · 2026Review
- Retrospective Validation of a Resource-Aware Assistive AI Tool for Screening Prostate Needle Biopsies and Classifying Prostate Adenocarcinoma into ISUP Grade Groups.Life (Basel, Switzerland) · 2026Article
- What practicing pathologists and oncologists should know about the new computational pathology-based companion diagnostic tools.The Journal of pathology · 2026Review
- An agentic framework for autonomous scientific discovery in cancer pathology.Nature medicine · 2026Article
- A cell comparative multiple instance learning network guided by image quality assessment for cervical whole slide image classification.iScience · 2026Article
- Lineage Classification of Pituitary Neuroendocrine Tumors From Whole-Slide Images Using Attention-Guided Graph Representation Learning.Endocrine pathology · 2026Article
- An AI framework for automated quality control of paraffin block and slide consistency: a clinical evaluation and human-machine comparison study.Virchows Archiv : an international journal of pathology · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Interpretative diagnostic model for neuroblastoma metastases using bone marrow cytology.Journal of translational medicine · 2026Article
- Fast organ-of-origin classification for digital pathology quality control.Journal of pathology informatics · 2026Article
- LazySlide: accessible and interoperable whole-slide image analysis.Nature methods · 2026Article
- Practical consequences of the European union-AI act for anatomic pathology laboratories a European society of pathology and European society of digital and integrative pathology commissioned expert opinion paper.Virchows Archiv : an international journal of pathology · 2026Review
- Adaptive diagnostic reasoning framework for pathology with multimodal large language models.Communications medicine · 2026Article
- HistoSweep enables cellular-resolution tissue quality control for gigapixel images in digital pathology and spatial omics.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Histological slides contain numerous artifacts that can significantly deteriorate the performance of image analysis algorithms. Here we develop the GrandQC tool for tissue and multi-class artifact segmentation. GrandQC allows for high-precision tissue segmentation (Dice score 0.957) and segmentation of tissue without artifacts (Dice score 0.919-0.938 dependent on magnification). Slides from 19 international pathology departments digitized with the most common scanning systems and from The Cancer Genome Atlas dataset were used to establish a QC benchmark, analyzing inter-institutional, intra-institutional, temporal, and inter-scanner slide quality variations. GrandQC improves the performance of downstream image analysis algorithms. We open-source the GrandQC tool, our large manually annotated test dataset, and all QC masks for the entire TCGA cohort to address the problem of QC in digital/computational pathology. GrandQC can be used as a tool to monitor sample preparation and scanning quality in pathology departments and help to track and eliminate major artifact sources.
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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 Socratic graph.