Zhang H, Wang P, Liu J, Qin J. pFedBCC: Personalizing Federated multi-target domain adaptive segmentation via Bi-pole Collaborative Calibration.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2025;
263:108635. [PMID:
39956050 DOI:
10.1016/j.cmpb.2025.108635]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2024] [Revised: 01/19/2025] [Accepted: 02/01/2025] [Indexed: 02/18/2025]
Abstract
BACKGROUND AND OBJECTIVE
Multi-target domain adaptation (MTDA) is a well-established technology for unsupervised segmentation. It can significantly reduce the workload of large-scale data annotations, but assumes that each domain data can be freely accessed. However, data privacy limit its deployment in real-world medical scenes. Aiming at this problem, federated learning (FL) commits a paradigm to handle private cross-institution data.
METHODS
This paper makes the first attempt to apply FedMTDA to medical image segmentation by proposing a personalized Federated Bi-pole Collaborative Calibration (pFedBCC) framework, which leverages unannotated private client data and a public source-domain model to learn a global model at the central server for unsupervised multi-type immunohistochemically (IHC) image segmentation. Concretely, pFedBCC tackles two significant challenges in FedMTDA including client-side prediction drift and server-side aggregation drift via Semantic-affinity-driven Personalized Label Calibration (SPLC) and Source-knowledge-oriented Consistent Gradient Calibration (SCGC). To alleviate local prediction drift, SPLC personalizes a cross-domain graph reasoning module for each client, which achieves semantic-affinity alignment between high-level source- and target-domain features to produce pseudo labels that are semantically consistent with source-domain labels to guide client training. To further alleviate global aggregation drift, SCGC develops a new conflict-gradient clipping scheme, which takes the source-domain gradient as a guidance to ensure that all clients update with similar gradient directions and magnitudes, thereby improving the generalization of the global model.
RESULTS
pFedBCC is evaluated on private and public IHC benchmarks, including the proposed MT-IHC dataset, and the panCK, BCData, DLBC-Morph and LYON19 datasets. Overall, pFedBCC achieves the best performance of 88.8% PA on MT-IHC, as well as 88.4% PA on the LYON19 dataset, respectively.
CONCLUSIONS
The proposed pFedBCC performs better than all comparison methods. The ablation study also confirms the contribution of SPLC and SCGC for unsupervised multi-type IHC image segmentation. This paper constructs a MT-IHC dataset containing more than 19,000 IHC images of 10 types (CgA, CK, Syn, CD, Ki67, P40, P53, EMA, TdT and BCL). Extensive experiments on the MT-IHC and public IHC datasets confirm that pFedBCC outperforms existing FL and DA methods.
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