ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2021
Quality control stress test for deep learning-based diagnostic model in digital pathology.
Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
What it found
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Who cites it
64 citing papers in PubMed.
- Current quality challenges in H&E preparation: The critical foundation for increased use of digital pathology and AI emergence.Virchows Archiv : an international journal of pathology · 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
- A protocol for evaluating robustness to H&E staining variation in computational pathology models.Journal of pathology informatics · 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
- Benchmarking foundation models as feature extractors for weakly supervised computational pathology.Nature biomedical engineering · 2026Article
- From model failure to system harm: operationalizing a sociotechnical pathway for healthcare AI safety.Frontiers in artificial intelligence · 2026Article
- Digital transformation of pathology - the European Society of Pathology expert opinion paper.Virchows Archiv : an international journal of pathology · 2025Article
- Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities.Journal of clinical medicine · 2025Article
- Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery.Current oncology (Toronto, Ont.) · 2025Review
- Multiple instance learning using pathology foundation models effectively predicts kidney disease diagnosis and clinical classification.Scientific reports · 2025Article
- Postponing evolution: why are we choosing to ignore the need for a digital transformation in pathology?Virchows Archiv : an international journal of pathology · 2025Review
- Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images.bioRxiv : the preprint server for biology · 2025Article
- Automated determination of tumor cell percentages in whole slide images: A nuclear classification study for molecular pathology tests.Journal of pathology informatics · 2025Article
- An end-to-end multifunctional AI platform for intraoperative diagnosis.NPJ digital medicine · 2025Article
- Lossy DICOM conversion may affect AI performance.Scientific reports · 2025Article
- Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging, and Classification.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Review
- Construction of prognostic scoring model for ovarian cancer based on deep learning algorithm.Discover oncology · 2025Article
- Jointly exploring client drift and catastrophic forgetting in dynamic learning.Scientific reports · 2025Article
- Spatial transcriptomic approaches for characterising the bone marrow landscape: pitfalls and potential.Leukemia · 2025Review
- Using digital pathology to standardize and automate histological evaluations of environmental samples.Environmental toxicology and chemistry · 2025Review
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Digital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections' thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study, we digitally reproduce major types of artifacts. Using six datasets from four different institutions digitized by different scanner systems, we systematically explore artifacts' influence on the accuracy of the pre-trained, validated, deep learning-based model for prostate cancer detection in histological slides. We provide evidence that any histological artifact dependent on severity can lead to a substantial loss in model performance. Strategies for the prevention of diagnostic model accuracy losses in the context of artifacts are warranted. Stress-testing of diagnostic models using synthetically generated artifacts might be an essential step during clinical validation of deep learning-based 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.