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Interpretable Roi Identification In Brain Image Analysis: Overcoming Cnn Black Box Challenges With Kriging-enhanced Adaptive Sampling.

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Interpretable Roi Identification In Brain Image Analysis: Overcoming Cnn Black Box Challenges With Kriging-enhanced Adaptive Sampling.

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Published on: July 11, 2025

Interpretable ROI Identification in Brain Image Analysis: Overcoming CNN Black Box Challenges With Kriging-Enhanced

HyunAh Lee¹, Jihnhee Yu¹, Soyun Park²

¹Department of Biostatistics, The State University of New York, University at Buffalo, Buffalo, New York, USA.

Statistics in Medicine

|June 23, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

We developed a new framework called adaptive spatial key-region identification (ASKRI) to improve brain image analysis. ASKRI enhances the accuracy and interpretability of identifying regions of interest, making diagnostic support more efficient.

Keywords:

adaptive sampling black‐box problem block‐to‐block kriging computational efficiency convolutional neural networks (CNNs)regions of interest (ROI)

Related Experiment Videos

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

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Published on: July 11, 2025

Area of Science:

Neuroimaging
Computational Neuroscience
Medical Image Analysis

Background:

Brain image analysis faces challenges in precision, computational efficiency, and interpretability.
Neural networks (CNNs) are effective but often act as black boxes, limiting clinical utility.
Interpreting complex patterns in brain scans remains a significant hurdle for widespread adoption.

Purpose of the Study:

To introduce the adaptive spatial key-region identification (ASKRI) framework for enhanced brain image analysis.
To improve the accuracy and interpretability of identifying regions of interest (ROIs) in brain scans.
To reduce computational burden in deep learning models for neuroimaging without compromising performance.

Main Methods:

ASKRI combines adaptive sampling (Shannon entropy), probability-mean-driven selection, and kriging for spatial uncertainty quantification.
It integrates block-to-block kriging with statistical inference to interpolate CNN-derived classification performance.
The framework is designed for seamless integration with convolutional neural networks (CNNs).

Main Results:

ASKRI significantly reduces computational load for model training while maintaining predictive accuracy.
The framework reliably identifies spatially consistent and biologically meaningful regions associated with aging in the TRACK-TBI dataset.
ASKRI enhances both accuracy and interpretability in region of interest identification.

Conclusions:

The ASKRI framework offers a novel, transparent, and resource-efficient approach to brain image analysis.
It has the potential to advance diagnostic support in clinical settings by improving ROI identification.
ASKRI addresses key limitations in current neuroimaging analysis, paving the way for more reliable clinical applications.