Detecting Spurious Correlations with Sanity Tests for Artificial Intelligence Guided Radiology Systems

TL;DR

This paper introduces sanity tests to detect when AI radiology systems perform well for incorrect reasons, aiming to improve confidence in their efficacy before costly validation studies.

Contribution

It proposes a series of sanity tests to identify spurious correlations in AI radiology systems, enhancing validation processes and reliability.

Findings

Sanity tests can reveal when AI models rely on spurious features.

Application to pancreatic cancer classification demonstrates the tests' effectiveness.

Sanity tests help prevent deployment of unreliable AI systems.

Abstract

Artificial intelligence (AI) has been successful at solving numerous problems in machine perception. In radiology, AI systems are rapidly evolving and show progress in guiding treatment decisions, diagnosing, localizing disease on medical images, and improving radiologists' efficiency. A critical component to deploying AI in radiology is to gain confidence in a developed system's efficacy and safety. The current gold standard approach is to conduct an analytical validation of performance on a generalization dataset from one or more institutions, followed by a clinical validation study of the system's efficacy during deployment. Clinical validation studies are time-consuming, and best practices dictate limited re-use of analytical validation data, so it is ideal to know ahead of time if a system is likely to fail analytical or clinical validation. In this paper, we describe a series of sanity tests to identify when a system performs well on development data for the wrong reasons. We illustrate the sanity tests' value by designing a deep learning system to classify pancreatic cancer seen in computed tomography scans.