PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy
Debesh Jha, Nikhil Kumar Tomar, Vanshali Sharma, Quoc-Huy Trinh,, Koushik Biswas, Hongyi Pan, Ritika K. Jha, Gorkem Durak, Alexander Hann,, Jonas Varkey, Hang Viet Dao, Long Van Dao, Binh Phuc Nguyen, Nikolaos, Papachrysos, Brandon Rieders, Peter Thelin Schmidt, Enrik Geissler

TL;DR
PolypDB is a large, multi-center, publicly available dataset of colonoscopy images from various modalities, designed to facilitate the development of AI algorithms for polyp detection and segmentation, addressing the lack of diverse datasets in this field.
Contribution
This paper introduces PolypDB, a comprehensive multi-center dataset with diverse imaging modalities, enabling improved AI development for colonoscopy analysis.
Findings
Benchmark results for detection and segmentation across modalities.
Federated learning experiments demonstrate dataset utility.
Dataset availability promotes research in polyp detection.
Abstract
Colonoscopy is the primary method for examination, detection, and removal of polyps. However, challenges such as variations among the endoscopists' skills, bowel quality preparation, and the complex nature of the large intestine contribute to high polyp miss-rate. These missed polyps can develop into cancer later, underscoring the importance of improving the detection methods. To address this gap of lack of publicly available, multi-center large and diverse datasets for developing automatic methods for polyp detection and segmentation, we introduce PolypDB, a large scale publicly available dataset that contains 3934 still polyp images and their corresponding ground truth from real colonoscopy videos. PolypDB comprises images from five modalities: Blue Light Imaging (BLI), Flexible Imaging Color Enhancement (FICE), Linked Color Imaging (LCI), Narrow Band Imaging (NBI), and White Light…
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Taxonomy
TopicsColorectal Cancer Screening and Detection · Radiomics and Machine Learning in Medical Imaging
