ArticleScientific data2026
CystoDS: a multiclass endoscopy image dataset for artificial intelligence-assisted bladder cancer detection.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Multimodal large language models for bladder tumor detection in cystoscopy: a retrospective benchmarking study.World journal of urology · 2026Article
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10 authors.
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Abstract
Cystoscopy is a common endoscopic procedure used for the visual inspection of the lower urinary tract, particularly for the detection and surveillance of bladder cancer. Artificial intelligence (AI) strategies could help address the recognized shortcomings of cystoscopy by identifying malignant and non-malignant regions of interest (ROIs) and providing real-time clinical decision support for biopsy and surgical resection. However, curating a dataset for training AI models is challenging and time-time consuming. We present CystoDS, a high-quality bladder imaging dataset derived from standard white light cystoscopy that is ready for AI applications for detection of bladder cancer and cancer-mimicking benign lesions. This dataset includes 8,067 images from 160 patients labelled with five classes and 22 subclasses, along with segmentation data for 768 of the images. We detail the methods used for image acquisition, the structure of the dataset, and our technical validation process to demonstrate the AI-readiness of the data.
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