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Related Experiment Video

Updated: Aug 11, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

A multi-centre polyp detection and segmentation dataset for generalisability assessment.

Sharib Ali^1,2,3, Debesh Jha^4,5,6, Noha Ghatwary⁷

¹School of Computing, University of Leeds, LS2 9JT, Leeds, United Kingdom. s.s.ali@leeds.ac.uk.

Scientific Data

|February 6, 2023

Summary

This summary is machine-generated.

This study introduces PolypGen, a large, multicenter dataset for colon polyp detection and segmentation. It addresses the need for robust AI models that generalize across diverse patient populations.

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Last Updated: Aug 11, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Gastroenterology

Background:

Colon polyps are recognized precursors to colon cancer, with their characteristics influencing cancer risk.
Automated polyp detection and segmentation methods require rigorous validation on diverse datasets.
Existing public datasets are insufficient for testing the generalizability of these AI methods.

Purpose of the Study:

To curate and present PolypGen, the most comprehensive public dataset for colon polyp detection and segmentation.
To facilitate the development and rigorous validation of AI models for colon polyp analysis.
To improve the generalizability of AI algorithms across different patient populations and clinical settings.

Main Methods:

Collected data from six unique medical centers, including over 300 patients.
Curated a dataset comprising single-frame and sequence data with 3762 annotated polyp labels.
Ensured precise polyp boundary delineation through verification by six senior gastroenterologists.

Main Results:

Developed PolypGen, a large-scale, multicenter dataset specifically for colon polyp detection and segmentation.
The dataset includes detailed annotations verified by expert gastroenterologists, ensuring high quality.
Provides insights into data construction, annotation strategies, quality assurance, and technical validation.

Conclusions:

PolypGen represents a significant advancement in public datasets for colon polyp research.
This resource is expected to accelerate the development of more robust and generalizable AI tools for colon cancer screening.
The dataset will enable rigorous testing and validation of automated polyp detection and segmentation algorithms.