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. Author manuscript; available in PMC: 2024 Jan 23.
Published in final edited form as: Nat Mach Intell. 2021 Dec 15;3(12):1081–1089. doi: 10.1038/s42256-021-00421-z

Fig. 2 |. Deployment and workflow of UCADI participants.

Fig. 2 |

a, Data: construct a local dataset based on the high-quality, well-annotated and anonymized CTs. b, Flow: the backbone of the 3D-DenseNet model mainly consists of six three-dimensional dense blocks (in green), two three-dimensional transmit blocks (in white) and an output layer (in grey). Computed tomography scans of each case are converted into a (16,128,128) tensor after adaptive sampling, decentralization and trilinear interpolation, and then fed into the three-dimensional CNN model for pneumonia classification. c, Process: during training, the model outputs are used to calculate the weighted cross-entropy to update the network parameters. While testing, five independent predictions of each case are incorporated to report the predictive diagnostic results.