March 3, 2026Open Access

MRI-Based Bladder Cancer Staging via YOLOv11 Segmentation and Deep Learning Classification

Key Points

Automated segmentation and classification enhance MRI-based bladder cancer staging feasibility, improving workflow efficiency.
The framework utilizes radiological reference labels to automate procedures, supporting deep learning's integration into clinical setting.
Validation with diverse datasets and patient-level data is crucial for establishing the model's generalizability and clinical relevance.
The approach emphasizes methodological consistency in staging techniques, potentially transforming standard practices in bladder cancer diagnosis.

Abstract

The proposed framework demonstrates the feasibility of automated MRI-based bladder cancer staging derived from radiological reference labels and supports the potential of deep learning for stand-ardizing and reproducing MRI-based staging procedures. Rather than serving as an independent clinical decision-support system, the framework is intended as a methodological and work-flow-oriented tool for automated staging consistency. Further validation using multi-center datasets, patient-level data splitting prior to augmentation, pathology-confirmed reference stand-ards, and explainable AI techniques is required to establish generalizability and clinical relevance.

MRI-Based Bladder Cancer Staging via YOLOv11 Segmentation and Deep Learning Classification

Key Points

Abstract

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