FedDA-TSformer: Federated Domain Adaptation with Vision TimeSformer for Left Ventricle Segmentation on Gated Myocardial Perfusion SPECT Image

no code implementations • 23 Feb 2025 • Yehong Huang, Chen Zhao, Rochak Dhakal, Min Zhao, Guang-Uei Hung, Zhixin Jiang, Weihua Zhou

This approach effectively addresses the dual requirements of federated learning and domain adaptation, enhancing the model's performance during training with multi-site datasets while ensuring the protection of data from different hospitals.

Domain Adaptation Federated Learning +2

Automatic reorientation by deep learning to generate short axis SPECT myocardial perfusion images

no code implementations • 7 Aug 2022 • Fubao Zhu, Guojie Wang, Chen Zhao, Saurabh Malhotra, Min Zhao, Zhuo He, Jianzhou Shi, Zhixin Jiang, Weihua Zhou

Five-fold cross-validation with 180 stress and 201 rest MPIs was used for training and internal validation; the remaining images were used for testing.

Model Optimization Translation

A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images

no code implementations • 7 Jun 2022 • Fubao Zhu, Jinyu Zhao, Chen Zhao, Shaojie Tang, Jiaofen Nan, Yanting Li, Zhongqiang Zhao, Jianzhou Shi, Zenghong Chen, Zhixin Jiang, Weihua Zhou

Conclusion: Our proposed method achieved a high accuracy in extracting LV myocardial contours and assessing LV function.