# Disease Severity Regression with Continuous Data Augmentation

## TL;DR

This paper introduces a continuous data augmentation method using a novel GAN to generate medical images at real-valued severity levels, improving disease severity regression accuracy.

## Contribution

It proposes a continuous severity GAN and dataset-disjoint multi-objective optimization to enhance medical image data augmentation for severity estimation.

## Key findings

- Achieved higher classification performance than conventional methods.
- Effectively generated images at real-valued severity levels.
- Improved disease severity regression accuracy.

## Abstract

Disease severity regression by a convolutional neural network (CNN) for medical images requires a sufficient number of image samples labeled with severity levels. Conditional generative adversarial network (cGAN)-based data augmentation (DA) is a possible solution, but it encounters two issues. The first issue is that existing cGANs cannot deal with real-valued severity levels as their conditions, and the second is that the severity of the generated images is not fully reliable. We propose continuous DA as a solution to the two issues. Our method uses continuous severity GAN to generate images at real-valued severity levels and dataset-disjoint multi-objective optimization to deal with the second issue. Our method was evaluated for estimating ulcerative colitis (UC) severity of endoscopic images and achieved higher classification performance than conventional DA methods.