Visual interpretability in 3D brain tumor segmentation network.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neuroscience

Background:

• 3D Convolutional Neural Networks (CNNs) excel at brain tumor segmentation but lack interpretability.
• The "black-box" nature of CNNs poses risks for clinical decision-making in healthcare.
• Accurate and transparent predictions are crucial for deploying deep learning in medicine.

Purpose of the Study:

• To develop and evaluate a 3D visual explanation technique for 3D CNN brain tumor segmentation models.
• To explore the benefits of gradient-free interpretability methods over gradient-based ones.
• To assess the coherence of model-generated explanations with expert domain knowledge.

Main Methods:

• Extended a post-hoc interpretability technique to generate 3D visual explanations.
• Employed a gradient-free approach to analyze model behavior.
• Quantitatively evaluated the interpretability methodology using the BraTS-2018 dataset.

Main Results:

• Generated 3D visual explanations revealing the segmentation model's prediction strategy.
• Demonstrated that the model's captured information aligns with human expert domain knowledge.
• Quantitatively validated the interpretability methodology, confirming the reliability of visual explanations.

Conclusions:

• The proposed 3D visual explanation method enhances the transparency and trustworthiness of brain tumor segmentation models.
• Gradient-free interpretability offers advantages for analyzing complex medical imaging AI.
• This approach facilitates the clinical integration of deep learning by providing understandable insights into model predictions.