ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2018
Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.The Journal of pathology · 2019Guideline
- A distribution-aware semi-supervised pipeline for cost-effective neuron segmentation.iScience · 2026Article
- A pathologist-AI collaboration framework for enhancing diagnostic accuracies and efficiencies.Nature biomedical engineering · 2025Article
- Mixed-Supervised Learning for Cell Classification.Sensors (Basel, Switzerland) · 2025Article
- A predicted-loss based active learning approach for robust cancer pathology image analysis in the workplace.BMC medical imaging · 2024Article
- Machine learning approaches for spatial omics data analysis in digital pathology: tools and applications in genitourinary oncology.Frontiers in oncology · 2024Review
- Deep Transfer Learning with Enhanced Feature Fusion for Detection of Abnormalities in X-ray Images.Cancers · 2023Article
- Efficient Segmentation for Left Atrium With Convolution Neural Network Based on Active Learning in Late Gadolinium Enhancement Magnetic Resonance Imaging.Journal of Korean medical science · 2022Article
- PathAL: An Active Learning Framework for Histopathology Image Analysis.IEEE transactions on medical imaging · 2022Article
- Digital Pathology and Artificial Intelligence Applications in Pathology.Brain tumor research and treatment · 2022Review
- Active Learning for Efficient Segmentation of Liver with Convolutional Neural Network-Corrected Labeling in Magnetic Resonance Imaging-Derived Proton Density Fat Fraction.Journal of digital imaging · 2021Article
- Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: Evaluation on kidney segmentation in abdominal CT.Scientific reports · 2020Article
- Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches.Frontiers in neuroscience · 2020Article
- Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review.World journal of gastrointestinal oncology · 2019Article
- Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl.Nature methods · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Segmentation of nuclei in whole slide tissue images is a common methodology in pathology image analysis. Most segmentation algorithms are sensitive to input algorithm parameters and the characteristics of input images (tissue morphology, staining, etc.). Because there can be large variability in the color, texture, and morphology of tissues within and across cancer types (heterogeneity can exist even within a tissue specimen), it is likely that a set of input parameters will not perform well across multiple images. It is, therefore, highly desired, and necessary in some cases, to carry out a quality control of segmentation results. This work investigates the application of machine learning in this process. We report on the application of active learning for segmentation quality assessment for pathology images and compare three classification methods, Support Vector Machine (SVM), Random Forest (RF) and Convolutional Neural Network (CNN), for their performance improvement and efficiency.
Identifiers
29888078PMC5961826W2994344014What 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.