ArticleScientific reports2018
A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Intradialytic hypotension and hemodynamic phenotypes in children following continuous renal replacement therapy initiation.Pediatric research · 2026Observational
- Similarity-guided swarm of models: enhancing semi-supervised learning in computational pathology.Scientific reports · 2025Article
- Assessment of transformer-based AI in clinical oncology.BMC medical informatics and decision making · 2025Article
- Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22.Critical care (London, England) · 2025Review
- A machine learning approach using gait parameters to cluster TKA subjects into stable and unstable joints for discovery analysis.The Knee · 2025Article
- Article
- Article
- Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions.Life (Basel, Switzerland) · 2025Review
- Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides.Nature communications · 2024Article
- A deep-learning strategy to identify cell types across species from high-density extracellular recordings.bioRxiv : the preprint server for biology · 2024Article
- Estimation of gestating sows' welfare status based on machine learning methods and behavioral data.Scientific reports · 2023Article
- Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance.Diagnostics (Basel, Switzerland) · 2023Review
- Automatic Classification of Histopathology Images across Multiple Cancers Based on Heterogeneous Transfer Learning.Diagnostics (Basel, Switzerland) · 2023Article
- ECG-Based Semi-Supervised Anomaly Detection for Early Detection and Monitoring of Epileptic Seizures.International journal of environmental research and public health · 2023Article
- Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics.Nanoscale advances · 2023Review
- Identifying infected patients using semi-supervised and transfer learning.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects.Journal of clinical medicine · 2022Review
- Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency.Physics in medicine and biology · 2022Review
- Histology segmentation using active learning on regions of interest in oral cavity squamous cell carcinoma.Journal of pathology informatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Completely labeled pathology datasets are often challenging and time-consuming to obtain. Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points with the help of a large number of unlabeled data points. In this paper, we investigated the possibility of using clustering analysis to identify the underlying structure of the data space for SSL. A cluster-then-label method was proposed to identify high-density regions in the data space which were then used to help a supervised SVM in finding the decision boundary. We have compared our method with other supervised and semi-supervised state-of-the-art techniques using two different classification tasks applied to breast pathology datasets. We found that compared with other state-of-the-art supervised and semi-supervised methods, our SSL method is able to improve classification performance when a limited number of labeled data instances are made available. We also showed that it is important to examine the underlying distribution of the data space before applying SSL techniques to ensure semi-supervised learning assumptions are not violated by the data.
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.