When To Adapt? Adapting the Model or Data in Federated Medical Imaging

• URL: http://arxiv.org/abs/2605.00892v1
• Date: Mon, 27 Apr 2026 20:17:50 GMT
• Title: When To Adapt? Adapting the Model or Data in Federated Medical Imaging
• Abstract summary: Federated learning enables collaborative model training across medical institutions without sharing raw data.<n>Existing approaches to address this challenge fall into two main paradigms: model-side personalization, and data-side harmonization.<n>We evaluate a broad set of state-of-the-art harmonization and personalization methods under a unified framework.
• Score: 1.8190930020764042
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning enables collaborative model training across medical institutions without sharing raw data, but its performance is often limited by domain heterogeneity across clients. Existing approaches to address this challenge fall into two main paradigms: model-side personalization, which adapts model parameters to each client, and data-side harmonization, which reduces inter-client variation at the input level. Despite their widespread use, these strategies have not been systematically compared. In this work, we conduct a comprehensive study across six medical imaging settings-colon polyp, skin lesion, and breast tumor segmentation, and tuberculosis CXR, brain tumor, and breast tumor classification-covering diverse types of domain shift. We evaluate a broad set of state-of-the-art harmonization and personalization methods under a unified framework. Our results reveal a conditional trade-off driven by the nature of heterogeneity: harmonization is more effective when variation is primarily appearance-based (e.g., CXR classification), while personalization performs better when differences are structural (e.g., colon polyp segmentation). When inter-client variation is limited, both strategies perform similarly. These findings demonstrate that the effectiveness of adaptation in federated medical imaging depends on the type and magnitude of domain shift rather than the strategy alone. We provide practical guidelines for selecting between harmonization and personalization and highlight directions for future hybrid approaches that combine both paradigms. Code is available at https://github.com/ChamaniS/WhenToAdapt.

Related papers

• Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization [0.0]
  In this work, we consider a realistic FL scenario where each client holds single-modality data (CT or MRI)<n>We evaluate convolution-based spatial augmentation, frequency-domain manipulation, domain-specific normalization, and global intensity nonlinear (GIN) augmentation.<n>Our federated approach achieves 93-98% of centralized training accuracy, demonstrating strong cross-modality generalization without compromising data privacy.
  arXiv  Detail & Related papers  (2026-02-24T11:13:01Z)
• U-Harmony: Enhancing Joint Training for Segmentation Models with Universal Harmonization [30.093279965784188]
  We propose a joint training method called Universal Harmonization (U-Harmony), which can be integrated into deep learning-based architectures with a domain-gated head.<n>By integrating U-Harmony, our approach sequentially normalizes and then denormalizes feature distributions to mitigate domain-specific variations.<n>More appealingly, our framework also supports universal modality adaptation, allowing the seamless learning of new imaging modalities and anatomical classes.
  arXiv  Detail & Related papers  (2026-01-21T02:43:39Z)
• Unsupervised Domain Adaptation via Content Alignment for Hippocampus Segmentation [5.118486547573804]
  This paper presents a novel unsupervised domain adaptation framework that addresses domain shifts encountered in cross-domain hippocampus segmentation from MRI.<n>Our approach combines efficient style harmonisation through z-normalisation with a bidirectional deformable image registration (DIR) strategy.<n>We validate our approach through comprehensive evaluations on both a synthetic dataset and three MRI hippocampus datasets representing populations with varying degrees of atrophy.
  arXiv  Detail & Related papers  (2025-10-15T01:34:41Z)
• Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI [5.631060921219683]
  Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data.<n>Traditional FL methods, such as Federated Averaging (FedAvg), face difficulties in generalizing across domains.<n>This paper introduces a novel approach that incorporates adaptive aggregation weights.
  arXiv  Detail & Related papers  (2024-10-29T20:53:01Z)
• PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation [51.509573838103854]
  We propose a semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation. Our PMT generates high-fidelity pseudo labels by learning robust and diverse features in the training process. Experimental results on two datasets with different modalities, i.e., CT and MRI, demonstrate that our method outperforms the state-of-the-art medical image segmentation approaches.
  arXiv  Detail & Related papers  (2024-09-08T15:02:25Z)
• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• A Twin Neural Model for Uplift [59.38563723706796]
  Uplift is a particular case of conditional treatment effect modeling. We propose a new loss function defined by leveraging a connection with the Bayesian interpretation of the relative risk. We show our proposed method is competitive with the state-of-the-art in simulation setting and on real data from large scale randomized experiments.
  arXiv  Detail & Related papers  (2021-05-11T16:02:39Z)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.