ArticleScientific reports2024
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 47 citations in OpenAlex.
- Stage-Aware Swin-Enhanced nnU-Net v2 for Robust Polyp Segmentation in Collaborative Endoscopic Visual Sensing.Sensors (Basel, Switzerland) · 2026Article
- BAASNet: boundary-aware deep learning for accurate polyp segmentation in colonoscopy.Scientific reports · 2026Article
- Uncertainty-Aware Prediction Across Endoscopic Domains: Laryngeal Narrow-Band and Gastrointestinal Imaging.Biomedicines · 2026Article
- Leveraging large language and vision models for knowledge extraction from large-scale image-text colonoscopy records.Nature biomedical engineering · 2026Article
- PrysmNet a polyp refining system using salience and multimodal guidance for reproducible cross domain segmentation.NPJ digital medicine · 2026Article
- Intelligent Detection Method of Defects in High-Rise Building Facades Using Infrared Thermography.Sensors (Basel, Switzerland) · 2026Article
- Computer-assisted detection of colorectal polyps: a narrative review of clinical utility, ongoing limitations, and opportunities for advancement.Translational gastroenterology and hepatology · 2026Review
- Deep multimodal state-space fusion of endoscopic-radiomic and clinical data for survival prediction in colorectal cancer.NPJ digital medicine · 2025Article
- Deep learning with refined single candidate optimizer for early polyp detection.Scientific reports · 2025Article
- Beyond Human Vision: Revolutionizing the Localization of Diminutive Sessile Polyps in Colonoscopy.Bioengineering (Basel, Switzerland) · 2025Article
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- Machine learning in healthcare citizen science: A scoping review.International journal of medical informatics · 2025Article
- Neural Radiance Fields for High-Fidelity Soft Tissue Reconstruction in Endoscopy.Sensors (Basel, Switzerland) · 2025Article
- Body Surface Potential Mapping: A Perspective on High-Density Cutaneous Electrophysiology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Artificial intelligence in advanced endoscopic imaging: transforming optical diagnosis in gastroenterology.Frontiers in medicine · 2025Review
- DeepNeXt: a lightweight polyp segmentation algorithm based on multi-scale attention.Quantitative imaging in medicine and surgery · 2024Article
- MedSegBench: A comprehensive benchmark for medical image segmentation in diverse data modalities.Scientific data · 2024Article
- MCH-PAN: gastrointestinal polyp detection model integrating multi-scale feature information.Scientific reports · 2024Article
- Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
32 authors at 20 institutions in 13 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.
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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.