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Related Experiment Video

Updated: Jan 11, 2026

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors
05:19

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

Published on: March 29, 2019

10.8K

Explainable and likelihood aware AI framework for MRI-based pixel-level bladder tumour prediction.

Muzammil Khan1, Antonius G de Groot2, Erik B Cornel3

  • 1Robotics and Mechatronics Group, University of Twente, 7522 NB, Enschede, The Netherlands. m.khan@utwente.nl.

Scientific Reports
|November 19, 2025
PubMed
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This summary is machine-generated.

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This study introduces an AI framework for detecting bladder tumours (BTs) using MRI scans. The Explainable and Likelihood-Aware AI (ELAAI) improves accuracy and transparency in cancer detection.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Bladder tumours (BTs) present diagnostic challenges due to high recurrence and progression rates.
  • Magnetic resonance imaging (MRI) offers potential for BT detection, but analysis requires advanced tools.
  • Current artificial intelligence (AI) models for MRI analysis face limitations in data availability, prediction accuracy, and transparency.

Purpose of the Study:

  • To develop an Explainable and Likelihood-Aware AI (ELAAI) framework for improved bladder tumour detection using MRI.
  • To address limitations of existing AI models, including data scarcity and lack of prediction transparency.
  • To enhance the reliability and clinical utility of AI in bladder cancer diagnostics.

Main Methods:

  • Developed ELAAI framework trained exclusively on normal bladder MRI scans.

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  • Integrated MFA-Net for multi-scale feature aggregation and bladder segmentation.
  • Incorporated an adaptive tolerance refinement step and SLIP-Net (a vision transformer) with a multi-scale deterministic uncertainty (MSDU) head for tumour likelihood prediction.

    • Main Results:

    • ELAAI demonstrated superior performance compared to state-of-the-art (SOTA) models.
    • The framework enhances transparency and reliability in AI-assisted clinical decision-making.
    • Successful segmentation and likelihood prediction of bladder tumours were achieved.

    Conclusions:

    • The ELAAI framework offers a novel and effective approach for AI-assisted bladder tumour detection via MRI.
    • ELAAI's explainability and likelihood prediction capabilities foster trust in clinical applications.
    • This AI framework has the potential to significantly improve early and accurate detection of bladder malignancies.