ArticleIEEE access : practical innovations, open solutions2021
Improving Colonoscopy Lesion Classification Using Semi-Supervised Deep Learning.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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
10 citing papers in PubMed.
- PolypSAM-Open: Mitigating Automation Bias in AI-Assisted Colonoscopy via Open-Set Surgical Artifact Rejection.Diagnostics (Basel, Switzerland) · 2026Article
- C3VDv2 - Colonoscopy 3D video dataset with enhanced realism.Scientific data · 2026Article
- Subclass-Aware Contrastive Semi-Supervised Learning for Inflammatory Bowel Disease Classification from Colonoscopy Images.Bioengineering (Basel, Switzerland) · 2025Article
- Attention-guided deep framework for polyp localization and subsequent classification via polyp local and Siamese feature fusion.Medical & biological engineering & computing · 2025Article
- GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification.IEEE journal of biomedical and health informatics · 2025Article
- Artificial intelligence-assisted colonoscopy for colorectal lesion detection: a case-control study on diagnostic accuracy and histopathological agreement.Arquivos brasileiros de cirurgia digestiva : ABCD = Brazilian archives of digestive surgery · 2025Article
- Artificial intelligence in colonoscopy: from detection to diagnosis.The Korean journal of internal medicine · 2024Review
- Spatio-temporal classification for polyp diagnosis.Biomedical optics express · 2023Article
- Artificial Intelligence for Colonoscopy: Past, Present, and Future.IEEE journal of biomedical and health informatics · 2022Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
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
While data-driven approaches excel at many image analysis tasks, the performance of these approaches is often limited by a shortage of annotated data available for training. Recent work in semi-supervised learning has shown that meaningful representations of images can be obtained from training with large quantities of unlabeled data, and that these representations can improve the performance of supervised tasks. Here, we demonstrate that an unsupervised jigsaw learning task, in combination with supervised training, results in up to a 9.8% improvement in correctly classifying lesions in colonoscopy images when compared to a fully-supervised baseline. We additionally benchmark improvements in domain adaptation and out-of-distribution detection, and demonstrate that semi-supervised learning outperforms supervised learning in both cases. In colonoscopy applications, these metrics are important given the skill required for endoscopic assessment of lesions, the wide variety of endoscopy systems in use, and the homogeneity that is typical of labeled datasets.
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.