Interpretable and Efficient Brain Image Analysis: Addressing CNN Black Box Challenge

Music City Center 

Brain image analysis is a rapidly advancing field, yet accurately identifying Regions of Interest (ROIs) remains challenging due to the limitations of traditional methods in precision, efficiency, and interpretability. While neural networks effectively handle large datasets and learn complex features, they often demand high computational resources, lengthy training times, and lack transparency.

To overcome these challenges, we propose an innovative method that enhances ROI identification accuracy and interpretability while improving computational efficiency. Our approach integrates classification-based uncertainty estimation and probability-driven techniques, employing adaptive sampling via Shannon entropy and a mean-based probability framework. Block kriging and statistical inference further enable efficient and precise hotspot detection, significantly reducing training time without sacrificing performance.

The proposed method integrates seamlessly with Convolutional Neural Networks (CNNs), offering accurate hotspot detection with reduced computational complexity. A subset of the Traumatic Brain Injury (TRACK-TBI) study dataset is analyzed to demonstrate its effectiveness.

Region of Interst(ROI)

Convolutional Neural Networks (CNNs)

Computational efficiency

Shannon Entropy

mean-based probability 

Section on Statistics in Imaging