ArticleData in brief2024
UIdataGB: Multi-Class ultrasound images dataset for gallbladder disease detection.
Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Multiple instance learning approach for automated gallbladder cancer detection using ultrasound imaging: multi-center validation of a deep learning model with the public dataset contribution.The Lancet regional health. Southeast Asia · 2026Article
- Gallbladder disease diagnosis from ultrasound using squeeze-and-excitation capsule network with convolutional bidirectional long short-term memory.Scientific reports · 2026Article
- GBCapsNet: A calibrated capsule network for automated gallbladder disease diagnosis via ultrasound imaging.PloS one · 2026Article
- MSFE-GallNet-X: a multi-scale feature extraction-based CNN Model for gallbladder disease analysis with enhanced explainability.BMC medical imaging · 2025Article
- Detection of Gallbladder Disease Types Using a Feature Engineering-Based Developed CBIR System.Diagnostics (Basel, Switzerland) · 2025Article
- Ultrasound image dataset for ovarian follicular development detection in pigs.Data in brief · 2024Article
- UIdataGB: Multi-Class ultrasound images dataset for gallbladder disease detection.Data in brief · 2024Article
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Authors and funding
5 authors.
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Abstract
Artificial Intelligence (AI) allows computers to self-develop decision-making algorithms through huge data analysis. In medical investigations, using computers to automatically diagnose diseases is a promising area of research that could change healthcare strategies worldwide. However, it can be challenging to reproduce or/and compare various approaches due to the often-limited datasets comprising medical images. Since there is no open access dataset for the Gallbladder (GB) organ, we introduce, in this study, a large dataset that includes 10,692 GB Ultrasound Images (UI) acquired at high resolution from 1,782 individuals. These UI include many disease types related to the GB, and they are organized around nine important anatomical landmarks. The data in this collection can be used to train machine learning (ML) and deep learning (DL) models for computer-aided detection of GB diseases. It can also help academics conduct comparative studies and test out novel techniques for analyzing UI to explore the medical domain of GB diseases. The objective is then to help move medical imaging forward so that patients get better treatment.
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