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Related Experiment Videos

Transformer-Driven Explainable Deep Learning with Quantitative Attribution Validation for Liver Tumor Detection.

Inzamam Mashood Nasir1, Hend Alshaya2, Sara Tehsin3

  • 1Human-Environment-Technology (HET) Systems Centre, Mykolas Romeris University, 08303 Vilnius, Lithuania.

Bioengineering (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces an explainable deep learning framework for reliable liver tumor detection on CT scans, significantly improving accuracy and localization performance over existing methods.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Liver tumor identification on CT scans faces challenges due to tumor heterogeneity, anatomical variations, and the opacity of deep learning models.
  • Existing deep learning models often lack interpretability, hindering clinical adoption for liver tumor detection.

Purpose of the Study:

  • To develop a reliable and explainable deep learning framework for liver tumor detection on computed tomography (CT) scans.
  • To enhance the interpretability of deep learning models in clinical settings for liver tumor identification.

Main Methods:

  • A novel deep learning framework combining Global Context (GC) fused with Transformer (Tf) and a Quantitative Attribution (QA) module was developed.
  • The framework utilizes gradient-based attribution with a localization module, evaluating spatial alignment without requiring segmentation supervision during training.
Keywords:
CT imagingattribution mappingexplainable AIliver tumor detectiontransformer models

Related Experiment Videos

  • Transformer-Encoders were employed to capture long-range dependencies, improving tumor detection performance.

    • Main Results:

    • The framework achieved high classification performance: 96.9% accuracy, 96.2% precision, 95.8% recall, 96.0% F1-score, 97.6% AUC, and 0.93 MCC.
    • Classification-based localization yielded an Intersection over Union (IoU) of 71.6% and a Dice coefficient of 83.5%.
    • Demonstrated significant performance improvements compared to existing Convolutional Neural Network (CNN) and Transformer-based systems.

    Conclusions:

    • The proposed deep learning framework offers a reliable and explainable solution for liver tumor detection on CT scans.
    • The integration of attribution mechanisms enhances qualitative evidence, facilitating clinical decision-making.
    • The framework shows superior performance and interpretability, paving the way for advanced AI applications in medical diagnostics.