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Handling imbalanced medical image data: A deep-learning-based one-class classification approach.

Long Gao¹, Lei Zhang², Chang Liu³

¹College of Computer, National University of Defense Technology, Changsha, 410073, China; Department of Radiology, School of Medicine, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA.

Artificial Intelligence in Medicine

|September 25, 2020

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This summary is machine-generated.

This study introduces a new deep learning method to improve outlier detection in imbalanced medical image datasets by capturing imaging complexity. The novel approach enhances feature learning for rare health event identification.

Area of Science:

Medical Imaging
Machine Learning
Data Science

Background:

Medical image datasets often exhibit class imbalance, hindering the accurate detection of rare health events (outliers).
Traditional classification methods struggle with imbalanced data, necessitating specialized approaches like one-class classification.
Existing one-class classification methods using feature mapping or fitting are often inadequate for complex medical image features.

Purpose of the Study:

To propose a novel deep learning method for optimal single-class feature learning in medical images.
To address the challenge of outlier detection in imbalanced medical image datasets.
To leverage the concept of imaging complexity for enhanced feature representation.

Main Methods:

A new deep learning approach is proposed that utilizes imaging complexity to learn inherent, single-class-relevant features.

Keywords:

Data imbalance Deep learning Image complexity Medical image classification

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The method involves applying perturbing operations to images to capture imaging complexity and improve feature learning.

The effectiveness of these perturbing operations is investigated and compared.

Main Results:

The proposed method demonstrates superior performance compared to four state-of-the-art methods.
Extensive experiments were conducted on four distinct clinical datasets to validate the findings.
The approach effectively enhances feature learning for outlier detection in imbalanced medical imaging.

Conclusions:

The novel method effectively captures imaging complexity for improved feature learning in medical images.
This approach offers a significant advancement in outlier detection for imbalanced clinical datasets.
The findings suggest a promising direction for handling rare health events in medical AI.