A deep learning knowledge distillation framework using knee MRI and arthroscopy data for meniscus tear detection.

ABSTRACT: Purpose: To construct a deep learning knowledge distillation framework exploring the utilization of MRI alone or combing with distilled Arthroscopy information for meniscus tear detection. Methods: A database of 199 paired knee Arthroscopy-MRI exams was used to develop a multimodal teacher network and an MRI-based student network, which used residual neural networks architectures. A knowledge distillation framework comprising the multimodal teacher network T and the monomodal student network S was proposed. We optimized the loss functions of mean squared error (MSE) and cross-entropy (CE) to enable the student network S to learn arthroscopic information from the teacher network T through our deep learning knowledge distillation framework, ultimatel

SUBMITTER: Ying M 

PROVIDER: S-EPMC10825958 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature