ArticleJournal of pathology informatics2021
Stress Testing Pathology Models with Generated Artifacts.
Article in Journal of pathology informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- 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
- Digital Pathology-Based Morphometric Analysis of Saccharomyces cerevisiae Effects on Splenic Cell Populations in Broiler Chickens.Probiotics and antimicrobial proteins · 2026Article
- Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images.bioRxiv : the preprint server for biology · 2025Article
- Improving quality control of whole slide images by explicit artifact augmentation.Scientific reports · 2024Article
- Tissue Contamination Challenges the Credibility of Machine Learning Models in Real World Digital Pathology.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2024Article
- Tissue contamination challenges the credibility of machine learning models in real world digital pathology.medRxiv : the preprint server for health sciences · 2023Article
- Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundMachine learning models provide significant opportunities for improvement in health care, but their "black-box" nature poses many risks.
methodsWe built a custom Python module as part of a framework for generating artifacts that are meant to be tunable and describable to allow for future testing needs. We conducted an analysis of a previously published digital pathology classification model and an internally developed kidney tissue segmentation model, utilizing a variety of generated artifacts including testing their effects. The artifacts simulated were bubbles, tissue folds, uneven illumination, marker lines, uneven sectioning, altered staining, and tissue tears.
resultsWe found that there is some performance degradation on the tiles with artifacts, particularly with altered stains but also with marker lines, tissue folds, and uneven sectioning. We also found that the response of deep learning models to artifacts could be nonlinear.
conclusionsGenerated artifacts can provide a useful tool for testing and building trust in machine learning models by understanding where these models might fail.
Indexed as
Identifiers
What OpenQuestion holds
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.