ArticleScientific reports2017
Crowdsourcing scoring of immunohistochemistry images: Evaluating Performance of the Crowd and an Automated Computational Method.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 8 citations in OpenAlex.
- A High-Throughput ImmunoHistoFluorescence (IHF) Method for Sub-Nuclear Protein Analysis in Tissue.Cells · 2025Article
- Crowdsourcing with the drift diffusion model of decision making.Scientific reports · 2024Article
- Synergizing Deep Learning-Enabled Preprocessing and Human-AI Integration for Efficient Automatic Ground Truth Generation.Bioengineering (Basel, Switzerland) · 2024Article
- Task design for crowdsourced glioma cell annotation in microscopy images.Scientific reports · 2024Article
- Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles.Acta neuropathologica communications · 2023Article
- Interactions between carbon nanotubes and external structures of SARS-CoV-2 using molecular docking and molecular dynamics.Journal of molecular structure · 2023Article
- Improving Crowdsourcing-Based Image Classification Through Expanded Input Elicitation and Machine Learning.Frontiers in artificial intelligence · 2022Article
- How the variability between computer-assisted analysis procedures evaluating immune markers can influence patients' outcome prediction.Histochemistry and cell biology · 2021Article
- Deep neural network models for computational histopathology: A survey.Medical image analysis · 2021Review
- Crawling the German Health Web: Exploratory Study and Graph Analysis.Journal of medical Internet research · 2020Article
- Development of an Automated Image Analyzer for Microvessel Density Measurement in Bone Marrow Biopsies.Annals of laboratory medicine · 2020Article
- Structured crowdsourcing enables convolutional segmentation of histology images.Bioinformatics (Oxford, England) · 2019Article
- Scientific Discovery Games for Biomedical Research.Annual review of biomedical data science · 2019Article
- Cognitive and Non-Cognitive Outcomes Associated with Student Engagement in a Novel Brain Chemoarchitecture Mapping Course-Based Undergraduate Research Experience.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2019Article
- Large-scale medical image annotation with crowd-powered algorithms.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Article
- Deep Learning and Its Applications in Biomedicine.Genomics, proteomics & bioinformatics · 2018Review
- A Review on the Applications of Crowdsourcing in Human Pathology.Journal of pathology informatics · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
The assessment of protein expression in immunohistochemistry (IHC) images provides important diagnostic, prognostic and predictive information for guiding cancer diagnosis and therapy. Manual scoring of IHC images represents a logistical challenge, as the process is labor intensive and time consuming. Since the last decade, computational methods have been developed to enable the application of quantitative methods for the analysis and interpretation of protein expression in IHC images. These methods have not yet replaced manual scoring for the assessment of IHC in the majority of diagnostic laboratories and in many large-scale research studies. An alternative approach is crowdsourcing the quantification of IHC images to an undefined crowd. The aim of this study is to quantify IHC images for labeling of ER status with two different crowdsourcing approaches, image-labeling and nuclei-labeling, and compare their performance with automated methods. Crowdsourcing- derived scores obtained greater concordance with the pathologist interpretations for both image-labeling and nuclei-labeling tasks (83% and 87%), as compared to the pathologist concordance achieved by the automated method (81%) on 5,338 TMA images from 1,853 breast cancer patients. This analysis shows that crowdsourcing the scoring of protein expression in IHC images is a promising new approach for large scale cancer molecular pathology studies.
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.