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数据集:训练集总共病例图像:1669,normal:468,abnormal:230,other:971
基础模型:EllipseNet
实验场景描述:2方参与的EllipseNet模型协同训练,数据划分成2份,各方数据集中normal、abnormal和other类型的数据量根据比例划分,具体比例见实验结果表。训练epoch=200,协同训练时每个epoch同步一次模型参数,采用平均融合策略
训练流程:
./experiments/exp1_client1_JointTrain_base_theta5_iou.sh
./experiments/exp1_client2_JointTrain_base_theta5_iou.sh
./src/server.py
实验结果:
结论:
If you find this project useful for your research, please use the following BibTeX entry.
Chen J., Zhang Y., Wang J., Zhou X., He Y., Zhang T. (2021) EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography. In: de Bruijne M. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science, vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_21
鹏城众智AI协同计算平台AISynergy是一个分布式智能协同计算平台。该平台的目标是通过智算网络基础设施使能数据、算力、模型、网络和服务,完成跨多个智算中心的协同计算作业,进而实现全新计算范式和业务场景,如大模型跨域协同计算、多中心模型聚合、多中心联邦学习等。
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