Solving Sample-Level Out-of-Distribution Detection on 3D Medical Images

TL;DR

This paper introduces a simple, histogram-based method for detecting out-of-distribution 3D medical images that outperforms complex deep learning approaches and achieves near-perfect results without requiring deep learning models.

Contribution

The paper proposes a novel histogram-based approach for 3D OOD detection that is computationally efficient and highly effective, outperforming existing DL-based methods.

Findings

Achieves 1.0 AUROC in most setups on public datasets.

Scores second in the Medical OOD challenge without fine-tuning.

Effectively detects sample-level OOD in 3D medical images.

Abstract

Deep Learning (DL) models tend to perform poorly when the data comes from a distribution different from the training one. In critical applications such as medical imaging, out-of-distribution (OOD) detection helps to identify such data samples, increasing the model's reliability. Recent works have developed DL-based OOD detection that achieves promising results on 2D medical images. However, scaling most of these approaches on 3D images is computationally intractable. Furthermore, the current 3D solutions struggle to achieve acceptable results in detecting even synthetic OOD samples. Such limited performance might indicate that DL often inefficiently embeds large volumetric images. We argue that using the intensity histogram of the original CT or MRI scan as embedding is descriptive enough to run OOD detection. Therefore, we propose a histogram-based method that requires no DL and achieves almost perfect results in this domain. Our proposal is supported two-fold. We evaluate the performance on the publicly available datasets, where our method scores 1.0 AUROC in most setups. And we score second in the Medical Out-of-Distribution challenge without fine-tuning and exploiting task-specific knowledge. Carefully discussing the limitations, we conclude that our method solves the sample-level OOD detection on 3D medical images in the current setting.