Explainability of deep neural networks for MRI analysis of brain tumors
By
Ramy A. Zeineldin
Mohamed E. Karar
Ziad Elshaer
·Jan Coburger
Christian R. Wirtz
Oliver Burgert
Franziska Mathis-Ullrich
April 23, 2022
Clinical Scorecard: Understanding the Interpretability of Deep Neural Networks in MRI Evaluation of Brain Tumors
At a Glance
Category Detail
Condition Brain and other nervous system tumors including glioblastoma Key Mechanisms Deep learning models applied to multimodal MRI for tumor detection and segmentation; explainable AI (XAI) methods to interpret model decisions Target Population Patients with brain tumors undergoing MRI evaluation Care Setting Radiology and neurosurgery clinical environments utilizing MRI and AI-assisted diagnosis
Key Highlights
Brain tumors are difficult to visually distinguish from surrounding tissue, complicating surgical resection. Deep learning models, especially CNNs like U-Net variants, improve tumor segmentation and classification from multimodal MRI. Explainable AI techniques are essential to provide transparency and trust in DL model predictions for clinical use. Guideline-Based Recommendations
Diagnosis
Use multimodal MRI (T1W, T1Gd, T2W, FLAIR) for improved tumor localization and boundary definition. Employ computer-aided diagnosis systems incorporating deep learning for enhanced tumor detection and grading. Management
Integrate explainable AI frameworks such as NeuroXAI to interpret DL model outputs without compromising performance. Use explainability methods to support clinical decision-making and intraoperative planning. Monitoring & Follow-up
Apply DL-assisted MRI evaluation for perioperative assessment and postoperative follow-up of brain tumors. Risks
Be aware of the 'black box' nature of DL models which may limit clinical adoption without explainability. Consider regulatory requirements such as GDPR mandating explanations for automated clinical decision systems. Patient & Prescribing Data
Patients with intracranial neoplasms undergoing MRI-based evaluation
DL models assist in tumor segmentation and histological grading, potentially guiding surgical and therapeutic interventions.
Clinical Best Practices
Utilize multimodal MRI sequences to capture comprehensive tumor characteristics. Incorporate explainable AI methods (e.g., Grad-CAM, integrated gradients) to visualize model decision areas. Prefer 3D explainability approaches over 2D to better represent volumetric brain tumor data. Ensure AI tools comply with data protection regulations requiring model transparency. References
