HIGHLIGHTS
- who: Isaac Shiri from the Centre , have published the Article: Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning, in the Journal: (JOURNAL)
- what: The authors aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images. The authors evaluated two FL models namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server as well as center-based (CB . . .
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