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Unsupervised pathology detection in medical images using conditional variational autoencoders.

Hristina Uzunova¹, Sandra Schultz², Heinz Handels²

¹Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. uzunova@imi.uni-luebeck.de.

International Journal of Computer Assisted Radiology and Surgery

|December 14, 2018

Summary

This study introduces an unsupervised method for pathology detection in medical images by learning normal data variations. The approach effectively identifies anomalies and enhances medical image registration processes.

Keywords:

Conditional variational autoencoder Image registration Unsupervised pathology detection

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Area of Science:

Medical Imaging Analysis
Machine Learning in Healthcare
Computational Pathology

Background:

Pathology detection in medical images is complex due to high variability.
Supervised methods require large annotated datasets, which are often unavailable.
Unsupervised approaches offer an alternative for pathology detection.

Purpose of the Study:

To develop an unsupervised method for pathology detection in medical image data.
To address the challenge of high variability in pathological structures.
To overcome the limitations of supervised learning due to data scarcity.

Main Methods:

Utilized conditional variational autoencoders to learn the distribution of healthy data.
Detected pathologies by identifying deviations from the learned norm.
Integrated prior knowledge about the data through conditional inputs.

Main Results:

Demonstrated suitability for pathology detection across 2D and 3D datasets.
Achieved reasonable performance metrics, including Dice coefficients and AUCs.
Showcased the ability to estimate missing correspondences in pathological images, improving registration.

Conclusions:

The presented unsupervised approach is effective for preliminary pathology detection in medical images.
The method serves as a valuable preprocessing step for other image processing techniques.
The approach enhances subsequent image registration tasks on pathological data.