A Benchmark of Medical Out of Distribution Detection

TL;DR

This paper benchmarks various Out-of-Distribution Detection methods across three medical imaging domains, revealing that simple classifiers often outperform complex methods, but all methods struggle with near-distribution images.

Contribution

It introduces a comprehensive benchmark for OoD detection in medical imaging and compares multiple methods across different domains and categories.

Findings

Simple binary classifiers perform best on average.

Most methods fail to detect images close to the training distribution.

Detection accuracy varies significantly across categories.

Abstract

Motivation: Deep learning models deployed for use on medical tasks can be equipped with Out-of-Distribution Detection (OoDD) methods in order to avoid erroneous predictions. However it is unclear which OoDD method should be used in practice. Specific Problem: Systems trained for one particular domain of images cannot be expected to perform accurately on images of a different domain. These images should be flagged by an OoDD method prior to diagnosis. Our approach: This paper defines 3 categories of OoD examples and benchmarks popular OoDD methods in three domains of medical imaging: chest X-ray, fundus imaging, and histology slides. Results: Our experiments show that despite methods yielding good results on some categories of out-of-distribution samples, they fail to recognize images close to the training distribution. Conclusion: We find a simple binary classifier on the feature representation has the best accuracy and AUPRC on average. Users of diagnostic tools which employ these OoDD methods should still remain vigilant that images very close to the training distribution yet not in it could yield unexpected results.