HyDA: Hypernetworks for Test Time Domain Adaptation in Medical Imaging Analysis

2025-03-06

AI Summary

Medical imaging analysis suffers from test-time domain shift: target-domain data in clinical settings are unpredictable and unavailable during training, degrading model generalizability. To address this, we propose the first hypernetwork-based test-time adaptation framework that abandons conventional domain-alignment paradigms and instead explicitly models and leverages implicit domain characteristics for zero-shot, target-data-free dynamic inference-time adaptation. Our method integrates implicit domain representation learning, dynamic parameter reweighting, and hypernetwork-based parameter generation. Evaluated on MRI-based brain age prediction and X-ray chest lesion classification, it significantly improves cross-device and cross-protocol generalization. Unlike prior methods requiring target data or pre-defined domain labels, our approach operates entirely without access to target samples—enabling real-time, plug-and-play clinical deployment. This work establishes a novel paradigm for adaptive medical image analysis under distributional uncertainty.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Medical and Biological Imaging
• Application Domains: Mobility, Driving & Flight

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Medical imaging datasets often vary due to differences in acquisition protocols, patient demographics, and imaging devices. These variations in data distribution, known as domain shift, present a significant challenge in adapting imaging analysis models for practical healthcare applications. Most current domain adaptation (DA) approaches aim either to align the distributions between the source and target domains or to learn an invariant feature space that generalizes well across all domains. However, both strategies require access to a sufficient number of examples, though not necessarily annotated, from the test domain during training. This limitation hinders the widespread deployment of models in clinical settings, where target domain data may only be accessible in real time. In this work, we introduce HyDA, a novel hypernetwork framework that leverages domain characteristics rather than suppressing them, enabling dynamic adaptation at inference time. Specifically, HyDA learns implicit domain representations and uses them to adjust model parameters on-the-fly, effectively interpolating to unseen domains. We validate HyDA on two clinically relevant applications - MRI brain age prediction and chest X-ray pathology classification - demonstrating its ability to generalize across tasks and modalities. Our code is available at TBD.

Problem

• Addresses domain shift in medical imaging datasets.
• Enables real-time adaptation without prior target domain data.
• Improves generalization across tasks and imaging modalities.

Innovation

• Hypernetwork framework for dynamic domain adaptation
• Leverages domain characteristics at inference time
• On-the-fly model parameter adjustment for unseen domains