ArticleScientific data2024
A multicenter bladder cancer MRI dataset and baseline evaluation of federated learning in clinical application.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Artificial intelligence and radiomics in bladder cancer MRI: a scoping review of applications, performance, and barriers to clinical translation.Abdominal radiology (New York) · 2026Article
- Artificial intelligence advances in cystoscopy and imaging for bladder cancer: a narrative review.The Canadian journal of urology · 2026Review
- Review
- A domain-adaptive deep contrastive network for magnetic resonance imaging-driven bladder cancer classification.NPJ digital medicine · 2026Article
- MRI-Based Bladder Cancer Staging via YOLOv11 Segmentation and Deep Learning Classification.Diseases (Basel, Switzerland) · 2026Article
- The Current Status and Evaluation of Artificial Intelligence in Bladder Cancer: Clinical Applications and Research.Computational and structural biotechnology journal · 2026Review
- Review
- Precise vesical wall staging of bladder cancer in the era of precision medicine: has it been fulfilled?Abdominal radiology (New York) · 2025Review
- Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformation.Scientific reports · 2025Article
- Synergy of PET and MR imaging in the local and regional staging of bladder cancer.Therapeutic advances in urologyReview
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bladder cancer (BCa), as the most common malignant tumor of the urinary system, has received significant attention in research on the clinical application of artificial intelligence algorithms. Nevertheless, it has been observed that certain investigations use data from various medical facilities to train models for BCa, which may pose a privacy risk. Given this concern, protecting patient privacy during machine learning algorithm training is a crucial aspect that requires substantial attention. One emerging machine learning paradigm that addresses this concern is federated learning (FL). FL enables multiple entities to collaboratively build machine learning models while preserving data privacy and security. In this study, we present a multicenter BCa magnetic resonance imaging (MRI) dataset. The dataset comprises 275 three-dimensional bladder T2-weighted MRI scans collected from four medical centers, and each scan includes diagnostic pathological labels for muscle invasion and pixel-level annotations of tumor contours. Four FL methods are used to assess the baseline of the dataset for both the task of diagnosing muscle-invasive bladder cancer and automatic bladder tumor lesion segmentation.
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Registered trials
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