Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging

TL;DR

This paper evaluates the effectiveness of auto-encoder-based deep generative models for detecting abnormalities in medical brain images, highlighting current challenges and providing a benchmark dataset for future research.

Contribution

It assesses the performance of variational and adversarial auto-encoders in abnormality detection and offers a benchmark dataset to facilitate further research in this area.

Findings

Deep generative models have limited success in abnormality detection.

Models trained on healthy data can identify outliers but with room for improvement.

The paper provides a pre-processed dataset for the research community.

Abstract

Recent advances in deep learning led to novel generative modeling techniques that achieve unprecedented quality in generated samples and performance in learning complex distributions in imaging data. These new models in medical image computing have important applications that form clinically relevant and very challenging unsupervised learning problems. In this paper, we explore the feasibility of using state-of-the-art auto-encoder-based deep generative models, such as variational and adversarial auto-encoders, for one such task: abnormality detection in medical imaging. We utilize typical, publicly available datasets with brain scans from healthy subjects and patients with stroke lesions and brain tumors. We use the data from healthy subjects to train different auto-encoder based models to learn the distribution of healthy images and detect pathologies as outliers. Models that can better learn the data distribution should be able to detect outliers more accurately. We evaluate the detection performance of deep generative models and compare them with non-deep learning based approaches to provide a benchmark of the current state of research. We conclude that abnormality detection is a challenging task for deep generative models and large room exists for improvement. In order to facilitate further research, we aim to provide carefully pre-processed imaging data available to the research community.