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Related Experiment Video

Updated: Jun 12, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Enhancing Deep Learning Model Explainability in Brain Tumor Datasets Using Post-Heuristic Approaches.

Konstantinos Pasvantis¹, Eftychios Protopapadakis¹

¹Department of Applied Informatics, University of Macedonia, Egnatia 156, 546 36 Thessaloniki, Greece.

Journal of Imaging

|September 27, 2024

Summary

This summary is machine-generated.

This study enhances deep learning interpretability for medical diagnosis using scenario-specific rules to refine LIME explanations, improving robustness in brain tumor detection.

Keywords:

brain tumor detection explainability trustworthiness

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Area of Science:

Artificial Intelligence
Medical Imaging
Machine Learning

Background:

Deep learning models show high efficacy in medical diagnosis.
A key limitation is the lack of explainability in their decision-making processes.
Enhancing model interpretability is crucial for clinical adoption.

Purpose of the Study:

To improve the robustness and interpretability of deep learning models in medical diagnosis.
To refine explanations generated by the LIME (Local Interpretable Model-agnostic Explanations) Library and LIME image explainer.
To develop a post-heuristic approach for more concrete diagnostic explanations.

Main Methods:

Utilized post-processing mechanisms based on scenario-specific rules.
Applied these methods to refine explanations from the LIME Library and LIME image explainer.
Conducted experiments using publicly accessible brain tumor detection datasets.

Main Results:

The proposed post-heuristic approach significantly advanced the interpretability of deep learning models.
Achieved more robust and concrete explanations in the context of medical diagnosis.
Demonstrated improved reliability for AI-driven diagnostic tools.

Conclusions:

The developed method enhances the trustworthiness of deep learning in medical diagnosis.
Scenario-specific rule-based post-processing offers a viable solution to the explainability problem.
This research contributes to more reliable and interpretable AI applications in healthcare.