Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Image Segmentation

• URL: http://arxiv.org/abs/2511.10945v1
• Date: Fri, 14 Nov 2025 04:15:34 GMT
• Title: Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Image Segmentation
• Authors: Xingyue Zhao, Wenke Huang, Xingguang Wang, Haoyu Zhao, Linghao Zhuang, Anwen Jiang, Guancheng Wan, Mang Ye,
• Abstract summary: Federated learning enables multiple medical institutions to train a global model without sharing data.<n>Current approaches primarily focus on final-layer features, overlooking critical multi-level cues.<n>We propose FedBCS to bridge feature representation gaps via domain-invariant contextual prototypes alignment.
• Score: 66.82598255715696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning enables multiple medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains a major challenge. Many existing works attempt to address this issue by leveraging model representations (e.g., mean feature vectors) to correct local training; however, they often face two key limitations: 1) Incomplete Contextual Representation Learning: Current approaches primarily focus on final-layer features, overlooking critical multi-level cues and thus diluting essential context for accurate segmentation. 2) Layerwise Style Bias Accumulation: Although utilizing representations can partially align global features, these methods neglect domain-specific biases within intermediate layers, allowing style discrepancies to build up and reduce model robustness. To address these challenges, we propose FedBCS to bridge feature representation gaps via domain-invariant contextual prototypes alignment. Specifically, we introduce a frequency-domain adaptive style recalibration into prototype construction that not only decouples content-style representations but also learns optimal style parameters, enabling more robust domain-invariant prototypes. Furthermore, we design a context-aware dual-level prototype alignment method that extracts domain-invariant prototypes from different layers of both encoder and decoder and fuses them with contextual information for finer-grained representation alignment. Extensive experiments on two public datasets demonstrate that our method exhibits remarkable performance.

Related papers

• Proto-Former: Unified Facial Landmark Detection by Prototype Transformer [77.47431726595111]
  Proto-Former is a unified, adaptive, end-to-end facial landmark detection framework.<n>It enables joint training across multiple datasets within a unified architecture.<n>Proto-Former achieves superior performance compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-10-17T06:00:25Z)
• Few to Big: Prototype Expansion Network via Diffusion Learner for Point Cloud Few-shot Semantic Segmentation [12.971351926107289]
  Prototype Expansion Network (PENet) is a framework that constructs big-capacity prototypes from two annotated feature sources.<n>PENet significantly outperforms state-of-the-art methods across various few-shot settings.
  arXiv  Detail & Related papers  (2025-09-16T09:29:46Z)
• Prototype-Aware Multimodal Alignment for Open-Vocabulary Visual Grounding [11.244257545057508]
  Prototype-Aware Multimodal Learning (PAML) is an innovative framework that addresses imperfect alignment between visual and linguistic modalities, insufficient cross-modal feature fusion, and ineffective utilization of semantic prototype information.<n>Our framework shows competitive performance in standard scene while achieving state-of-the-art results in open-vocabulary scene.
  arXiv  Detail & Related papers  (2025-09-08T02:27:10Z)
• Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation [13.24093379138835]
  Generalizable semantic segmentation aims to perform well on unseen target domains.<n>Class-wise prototypes serve as domain-invariant cues that benefit generalization due to their stability and semantic consistency.<n>We propose a novel framework for generalizable semantic segmentation: Prototypical Progressive Alignment and Reweighting.
  arXiv  Detail & Related papers  (2025-07-16T06:42:21Z)
• Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning [58.837146720228226]
  Few-shot learning aims to recognize novel queries with limited support samples by learning from base knowledge. Recent progress in this setting assumes that the base knowledge and novel query samples are distributed in the same domains. We propose to address the cross-domain few-shot learning problem where only extremely few samples are available in target domains.
  arXiv  Detail & Related papers  (2023-06-18T09:52:16Z)
• Contrast, Stylize and Adapt: Unsupervised Contrastive Learning Framework for Domain Adaptive Semantic Segmentation [18.843639142342642]
  We present CONtrastive FEaTure and pIxel alignment for bridging the domain gap at both the pixel and feature levels. Our experiments demonstrate that our method outperforms existing state-of-the-art methods using DeepLabV2.
  arXiv  Detail & Related papers  (2023-06-15T12:50:46Z)
• Dual Prototypical Contrastive Learning for Few-shot Semantic Segmentation [55.339405417090084]
  We propose a dual prototypical contrastive learning approach tailored to the few-shot semantic segmentation (FSS) task. The main idea is to encourage the prototypes more discriminative by increasing inter-class distance while reducing intra-class distance in prototype feature space. We demonstrate that the proposed dual contrastive learning approach outperforms state-of-the-art FSS methods on PASCAL-5i and COCO-20i datasets.
  arXiv  Detail & Related papers  (2021-11-09T08:14:50Z)
• Part-aware Prototype Network for Few-shot Semantic Segmentation [50.581647306020095]
  We propose a novel few-shot semantic segmentation framework based on the prototype representation. Our key idea is to decompose the holistic class representation into a set of part-aware prototypes. We develop a novel graph neural network model to generate and enhance the proposed part-aware prototypes.
  arXiv  Detail & Related papers  (2020-07-13T11:03:09Z)
• Cross-domain Detection via Graph-induced Prototype Alignment [114.8952035552862]
  We propose a Graph-induced Prototype Alignment (GPA) framework to seek for category-level domain alignment. In addition, in order to alleviate the negative effect of class-imbalance on domain adaptation, we design a Class-reweighted Contrastive Loss. Our approach outperforms existing methods with a remarkable margin.
  arXiv  Detail & Related papers  (2020-03-28T17:46:55Z)

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.