Navigating Distribution Shifts in Medical Image Analysis: A Survey

2024-11-05

arXiv.org

AI Summary

Deep learning models in medical image analysis often suffer from poor generalizability due to distribution shifts across hospitals, regions, or patient populations. This paper presents a systematic review of robust modeling strategies addressing this challenge, introducing— for the first time—a clinical-deployment–oriented taxonomy grounded in real-world constraints such as data availability, privacy preservation, and inter-institutional collaboration agreements, thereby moving beyond conventional purely technical taxonomies. Based on this framework, we categorize solutions into four deployment paradigms: joint training, federated learning, fine-tuning, and domain generalization—emphasizing synergistic optimization of cross-institutional modeling and privacy security. We rigorously delineate the applicability boundaries and practical bottlenecks of each paradigm and propose a deployable, clinically grounded roadmap for medical AI evolution—bridging the gap between laboratory research and diverse real-world healthcare settings.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Medical and Biological Imaging
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Security and privacy of machine learning and AI applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Medical Image Analysis (MedIA) has become indispensable in modern healthcare, enhancing clinical diagnostics and personalized treatment. Despite the remarkable advancements supported by deep learning (DL) technologies, their practical deployment faces challenges due to distribution shifts, where models trained on specific datasets underperform across others from varying hospitals, regions, or patient populations. To navigate this issue, researchers have been actively developing strategies to increase the adaptability and robustness of DL models, enabling their effective use in unfamiliar and diverse environments. This paper systematically reviews approaches that apply DL techniques to MedIA systems affected by distribution shifts. Unlike traditional categorizations based on technical specifications, our approach is grounded in the real-world operational constraints faced by healthcare institutions. Specifically, we categorize the existing body of work into Joint Training, Federated Learning, Fine-tuning, and Domain Generalization, with each method tailored to distinct scenarios caused by Data Accessibility, Privacy Concerns, and Collaborative Protocols. This perspective equips researchers with a nuanced understanding of how DL can be strategically deployed to address distribution shifts in MedIA, ensuring diverse and robust medical applications. By delving deeper into these topics, we highlight potential pathways for future research that not only address existing limitations but also push the boundaries of deployable MedIA technologies.

Problem

• Addressing distribution shifts in medical image analysis models
• Enhancing deep learning adaptability across diverse healthcare datasets
• Reviewing strategies for robust medical AI deployment in real-world settings

Innovation

• Joint Training for diverse data adaptation
• Federated Learning addressing privacy concerns
• Domain Generalization enhancing model robustness