# Detecting Shortcuts in Medical Images -- A Case Study in Chest X-rays

Source: [Detecting Shortcuts in Medical Images -- A Case Study in Chest X-rays](<https://www.sota2.com/research/paper/68fa7b941e209131cea893e1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:43:27.824Z

Authors: Amelia Jim\\'enez-S\\'anchez, Dovile Juodelyte, Bethany Chamberlain, Veronika Cheplygina

Published: 2022-11-08T14:36:33.000Z

## Abstract

The availability of large public datasets and the increased amount of computing power have shifted the interest of the medical community to high-performance algorithms. However, little attention is paid to the quality of the data and their annotations. High performance on benchmark datasets may be reported without considering possible shortcuts or artifacts in the data, besides, models are not tested on subpopulation groups. With this work, we aim to raise awareness about shortcuts problems. We validate previous findings, and present a case study on chest X-rays using two publicly available datasets. We share annotations for a subset of pneumothorax images with drains. We conclude with general recommendations for medical image classification.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.04279>)

[PDF](<https://arxiv.org/pdf/2211.04279.pdf>)