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Updated: Nov 19, 2025

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BRAIN LESION DETECTION USING A ROBUST VARIATIONAL AUTOENCODER AND TRANSFER LEARNING.

Haleh Akrami¹, Anand A Joshi¹, Jian Li¹

¹Signal and Image Processing Institute, University of Southern California, Los Angeles.

Proceedings. IEEE International Symposium on Biomedical Imaging

|January 27, 2021

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This study introduces a robust variational autoencoder and transfer learning to improve automated brain lesion detection from MRI scans. The method enhances accuracy, even with outlier data and varying imaging parameters.

Area of Science:

Medical Imaging
Machine Learning
Neurology

Background:

Automated brain lesion detection from multi-spectral MRI aids clinicians but supervised methods require extensive manual data and lack generalizability.
Unsupervised models like autoencoders eliminate the need for manual delineations but struggle with pre-trained model adaptation to new datasets with different parameters, demographics, or preprocessing.
Clinical datasets often contain anomalies, posing challenges for unsupervised learning due to outlier impact on model performance.

Purpose of the Study:

To address the challenges of unsupervised brain lesion detection, particularly concerning dataset variability and outliers.
To develop a robust and adaptable unsupervised learning framework for accurate brain lesion detection.

Main Methods:

A robust variational autoencoder (VAE) model utilizing β-divergence was employed to handle datasets with outliers.

Keywords:

anomaly detection brain imaging lesion detection robust variational autoencoders unsupervised machine learning variational autoencoders

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Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

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A transfer-learning approach was implemented to enable model adaptation across datasets with differing characteristics.

The combined approach was tested on MRI datasets for brain lesion detection.

Main Results:

The proposed method demonstrated improved accuracy in brain lesion detection.
The robust VAE effectively handled data containing anomalies and outliers.
Transfer learning facilitated successful adaptation of models to new datasets.

Conclusions:

Adapting robust statistical models and transfer learning within a VAE framework significantly enhances unsupervised brain lesion detection accuracy.
The developed method offers a more reliable solution for automated lesion detection in diverse clinical settings.
This approach overcomes key limitations of existing unsupervised methods, paving the way for broader clinical application.