Polyp-Size: A Precise Endoscopic Dataset for AI-Driven Polyp Sizing

Colorectal cancer often arises from precancerous polyps, where accurate size assessment is vital for clinical decisions but challenged by subjective methods. While artificial intelligence (AI) has shown promise in improving the accuracy of polyp size estimation, its development depends on large, meticulously annotated datasets. We present Polyp-Size, a dataset of 42 high-resolution white-light colonoscopy videos with polyp sizes precisely measured post-resection using vernier calipers to submillimeter precision. Unlike existing datasets primarily focused on polyp detection or segmentation, Polyp-Size offers validated size annotations, diverse polyp features (Paris classification, anatomical location and histological type), and standardized video formats, enabling robust AI models for size estimation. By making this resource publicly available, we aim to foster research collaboration and innovation in automated polyp measurement to ultimately improve clinical practice.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-05-31
DOI
https://doi.org/10.1038/s41597-025-05251-x
Citations
7
Primary Topic
Colorectal Cancer Screening and Detection
Type
article
Field-Weighted Citation Impact
7.32

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Polyp-Size: A Precise Endoscopic Dataset for AI-Driven Polyp Sizing

Yiming Song, Xiaolu Lin, Zhengjie Zhang, Hao Yan et al.
7 citations
Scientific Data
Colorectal Cancer Screening and Detection
7.32
article

Polyp-Size: A Precise Endoscopic Dataset for AI-Driven Polyp Sizing

Yiming Song, Xiaolu Lin, Zhengjie Zhang, Hao Yan, Biaohua Chen, Liuyi Yang, Xiaobo Li, Zeyu Li, Qingwei Zhang, Sijia Du, Ruilan Wang, Fei Liu, Dahong Qian, Li Zhao
article en
7 citations

Abstract

Colorectal cancer often arises from precancerous polyps, where accurate size assessment is vital for clinical decisions but challenged by subjective methods. While artificial intelligence (AI) has shown promise in improving the accuracy of polyp size estimation, its development depends on large, meticulously annotated datasets. We present Polyp-Size, a dataset of 42 high-resolution white-light colonoscopy videos with polyp sizes precisely measured post-resection using vernier calipers to submillimeter precision. Unlike existing datasets primarily focused on polyp detection or segmentation, Polyp-Size offers validated size annotations, diverse polyp features (Paris classification, anatomical location and histological type), and standardized video formats, enabling robust AI models for size estimation. By making this resource publicly available, we aim to foster research collaboration and innovation in automated polyp measurement to ultimately improve clinical practice.

Scientific DataVol. 12(1)
Fujian Medical University (CN), Harbin Medical University (CN), Shanghai Jiao Tong University (CN), Renji Hospital (CN), Xinjiang Production and Construction Corps (CN), Chinese People's Armed Police General Hospital (CN), Second Affiliated Hospital of Harbin Medical University (CN), Fujian Provincial Hospital (CN)
Science and Technology Commission of Shanghai Municipality
Openalex Percentile: Top 3%
Colorectal Cancer Screening and Detection
7.32
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.