Clinical Report: Explainability of Deep Neural Networks in Brain Tumor MRI Evaluation

Overview

Deep learning (DL) techniques, particularly convolutional neural networks, have shown promise in brain tumor detection and segmentation using multimodal MRI. However, their clinical adoption is limited by the lack of interpretability. The NeuroXAI framework offers a novel approach to generate 2D and 3D explainable sensitivity maps, enhancing transparency and trust in DL models for brain imaging.

Background

Brain tumors, including glioblastoma, are a leading cause of cancer mortality in adults and children. MRI is essential for tumor localization but interpreting multimodal images is challenging and time-sensitive. Computer-aided diagnosis systems using DL can assist clinicians by improving tumor detection and classification. Despite advances, DL models remain 'black boxes,' limiting clinical trust and regulatory acceptance. Explainable AI (XAI) methods aim to clarify DL decision-making, which is critical for sensitive applications like brain tumor evaluation.

Data Highlights

Recent DL segmentation methods such as U-Net variants have achieved state-of-the-art results in medical image segmentation. Gradient-based XAI methods like Grad-CAM and integrated gradients provide post hoc explanations without compromising model accuracy. Prior studies applied 2D Grad-CAM for brain tumor classification but lacked 3D analysis. NeuroXAI advances this by generating both 2D and 3D sensitivity maps to better visualize model decision areas in volumetric MRI data.

Key Findings

• Brain tumors are difficult to distinguish visually from surrounding tissue, complicating surgical resection.
• Multimodal MRI sequences (T1W, T1Gd, T2W, FLAIR) improve tumor visualization but are complex to interpret.
• DL models, especially CNNs like U-Net, enhance tumor segmentation and classification accuracy.
• Lack of transparency in DL models hinders clinical adoption and violates regulatory requirements like GDPR.
• NeuroXAI framework enables interpretable 2D and 3D sensitivity maps without altering DL model architecture or performance.
• Gradient-based XAI methods offer fast, post hoc explanations suitable for clinical workflows.

Clinical Implications

The NeuroXAI framework facilitates clinicians’ understanding of DL model predictions by providing clear visual explanations, potentially increasing trust and adoption in clinical practice. This transparency supports safer perioperative decision-making and postoperative monitoring. Incorporating explainability aligns with regulatory standards, promoting responsible AI integration in brain tumor management.

Conclusion

NeuroXAI represents a significant advancement in making DL models for brain tumor MRI evaluation interpretable, addressing a critical barrier to clinical implementation. By enhancing model transparency through 2D and 3D explainability, it supports improved clinical decision-making and patient outcomes.

References

1. Windisch et al. 2020 -- Application of 2D Grad-CAM for Brain Tumor Classification
2. European Union GDPR 2018 -- Data Protection Regulation for Automated Learning Systems
3. Ronneberger et al. 2015 -- U-Net: Convolutional Networks for Biomedical Image Segmentation
4. Selvaraju et al. 2017 -- Grad-CAM: Visual Explanations from Deep Networks