Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy.

Skylar S Gay,Kelly D Kisling,Brian M Anderson,Lifei Zhang,Dong Joo Rhee,Callistus Nguyen,Tucker Netherton,Jinzhong Yang,Kristy Brock,Anuja Jhingran,Hannah Simonds,Ann Klopp,Beth M Beadle,Laurence E Court,Carlos E Cardenas

Journal of applied clinical medical physics（2023）

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摘要

DeepLabv3+ and D-LinkNet are most robust to initial hyperparameter selection. Learning rate, nonlinear activation function, and kernel size are also important hyperparameters for improving performance.

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关键词

automatic segmentation, cervical cancer, digitally reconstructed radiograph, radiotherapy

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