FedDA-TSformer: Federated Domain Adaptation with vision TimeSformer for left ventricle segmentation on gated myocardial perfusion SPECT image

FedDA-TSformer provides a robust, privacy-preserving solution for multi-center left ventricular segmentation, outperforming traditional FedAvg in handling domain shifts. By leveraging the TimeSformer architecture and domain adaptation mechanisms, the framework ensures spatial-temporal consistency and data security across heterogeneous clinical sites. Despite current limitations regarding communication overhead and its focus on a small SPECT-only dataset, this study establishes a scalable foundation for collaborative cardiac diagnosis. Future work will prioritize model compression, asynchronous updates, and cross-modality generalization to CT and MRI to enhance its practicality in resource-constrained environments.

Authors

Institutions

Publication Details

Journal
BMC Methods
Published
2026-02-01
DOI
https://doi.org/10.1186/s44330-026-00057-8
Primary Topic
ECG Monitoring and Analysis
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

FedDA-TSformer: Federated Domain Adaptation with vision TimeSformer for left ventricle segmentation on gated myocardial perfusion SPECT image

Rochak Dhakal, Guang-Uei Hung, Min Zhao, Zhixin Jiang et al.
BMC Methods
ECG Monitoring and Analysis
article

FedDA-TSformer: Federated Domain Adaptation with vision TimeSformer for left ventricle segmentation on gated myocardial perfusion SPECT image

Rochak Dhakal, Guang-Uei Hung, Min Zhao, Zhixin Jiang, Yehong Huang, Chen Zhao, Weihua Zhou
article en

Abstract

FedDA-TSformer provides a robust, privacy-preserving solution for multi-center left ventricular segmentation, outperforming traditional FedAvg in handling domain shifts. By leveraging the TimeSformer architecture and domain adaptation mechanisms, the framework ensures spatial-temporal consistency and data security across heterogeneous clinical sites. Despite current limitations regarding communication overhead and its focus on a small SPECT-only dataset, this study establishes a scalable foundation for collaborative cardiac diagnosis. Future work will prioritize model compression, asynchronous updates, and cross-modality generalization to CT and MRI to enhance its practicality in resource-constrained environments.

BMC MethodsVol. 3(1)
Michigan Technological University (US), Central South University (CN), Kennesaw State University (US), Chang Bing Show Chwan Memorial Hospital (TW), Third Xiangya Hospital (CN), Nanjing Medical University (CN)
American Heart Association, Kennesaw State University
Openalex Percentile: Top 70%
ECG Monitoring and Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.