Automatic dataset shift identification to support root cause analysis of AI performance drift

2024-11-12

arXiv.org

AI Summary

In AI deployment for medical imaging, data distribution shifts frequently cause abrupt performance degradation and increased misdiagnosis risk. Existing methods can only detect the presence of shift but fail to identify its specific type—e.g., covariate shift, prior (concept) shift, or compound shift—hindering root-cause analysis and targeted mitigation. This paper proposes the first unsupervised framework for data shift type identification. It introduces a novel joint shift detection mechanism that synergistically leverages self-supervised encoder representations and task-model outputs. By integrating feature distribution comparison, unsupervised clustering, and multimodal image modeling, the method achieves high-accuracy shift-type discrimination across three major imaging modalities—chest X-ray, mammography, and fundus photography—and five realistic shift scenarios. Evaluated on four large public medical imaging datasets, it significantly enhances the robustness and interpretability of clinical AI systems.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Shifts in data distribution can substantially harm the performance of clinical AI models. Hence, various methods have been developed to detect the presence of such shifts at deployment time. However, root causes of dataset shifts are varied, and the choice of shift mitigation strategies is highly dependent on the precise type of shift encountered at test time. As such, detecting test-time dataset shift is not sufficient: precisely identifying which type of shift has occurred is critical. In this work, we propose the first unsupervised dataset shift identification framework, effectively distinguishing between prevalence shift (caused by a change in the label distribution), covariate shift (caused by a change in input characteristics) and mixed shifts (simultaneous prevalence and covariate shifts). We discuss the importance of self-supervised encoders for detecting subtle covariate shifts and propose a novel shift detector leveraging both self-supervised encoders and task model outputs for improved shift detection. We report promising results for the proposed shift identification framework across three different imaging modalities (chest radiography, digital mammography, and retinal fundus images) on five types of real-world dataset shifts, using four large publicly available datasets.

Problem

• Identify diverse dataset shifts in medical imaging AI
• Distinguish prevalence, covariate, and mixed shifts unsupervised
• Improve shift detection using self-supervised encoders

Innovation

• Unsupervised framework identifies dataset shift types
• Self-supervised encoders detect subtle covariate shifts
• Leverages task model outputs for improved shift detection