ArticleScientific reports2022
Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 38 citations in OpenAlex.
- Synthetic data generation: challenges and perspectives for gastrointestinal medicine.Nature reviews. Gastroenterology & hepatology · 2026Review
- A Systematic Review on Synthetic Medical Images Generation-Recent Trends and Future Opportunities.Diagnostics (Basel, Switzerland) · 2026Review
- Generative AI-Assisted Indication of Anatomical Landmarks to Enhance Safety under Intraoperative Bleeding in Laparoscopic Gastrectomy for Gastric Cancer.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2026Article
- Generative adversarial networks for balancing and expanding data resources for computer-aided detection in colonoscopy.Frontiers in medical technology · 2026Article
- Dual-generative synthesis framework: enhancing polyp segmentation in colonoscopy via mask-conditional GANs.Frontiers in oncology · 2026Article
- Assessing realism of artificial intelligence-generated colorectal polyp images: International multicenter blinded reader study.Endoscopy international open · 2026Article
- Clinical evaluation of medical image synthesis: a case study in wireless capsule endoscopy.Scientific reports · 2025Article
- Effectiveness of a novel artificial intelligence-assisted colonoscopy system for adenoma detection: a prospective, propensity score-matched, non-randomized controlled study in Korea.Clinical endoscopy · 2025Article
- Development and validation of an endoscopic diagnostic model for sessile serrated lesions based on machine learning algorithms.Frontiers in medicine · 2025Article
- A prospective comparison of two computer aided detection systems with different false positive rates in colonoscopy.NPJ digital medicine · 2024Article
- Impact of User's Background Knowledge and Polyp Characteristics in Colonoscopy with Computer-Aided Detection.Gut and liver · 2024Article
- Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography.BMC medical informatics and decision making · 2024Article
- Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning.Scientific reports · 2024Article
- Deep learning-powered generation of artificial endoscopic images of GI tract ulcers.iGIE : innovation, investigation and insights · 2023Article
- ChampKit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification.Computer methods and programs in biomedicine · 2023Article
- URNet: System for recommending referrals for community screening of diabetic retinopathy based on deep learning.Experimental biology and medicine (Maywood, N.J.) · 2023Article
Corrections and comments
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Authors and funding
16 authors at 2 institutions in 2 countries.
Funding
Abstract
Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We propose a polyp detection system that reflects the morphological characteristics of SSLs to detect unrecognized or easily missed polyps. To develop a well-trained system with imbalanced polyp data, a generative adversarial network (GAN) was used to synthesize high-resolution whole endoscopic images, including SSL. Quantitative and qualitative evaluations on GAN-synthesized images ensure that synthetic images are realistic and include SSL endoscopic features. Moreover, traditional augmentation methods were used to compare the efficacy of the GAN augmentation method. The CADe system augmented with GAN synthesized images showed a 17.5% improvement in sensitivity on SSLs. Consequently, we verified the potential of the GAN to synthesize high-resolution images with endoscopic features and the proposed system was found to be effective in detecting easily missed polyps during a colonoscopy.
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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.