The invention belongs to the technical field of medical
image segmentation, and discloses a medical
image segmentation model knowledge
distillation method and
system, and the method comprises the steps: introducing a
distillation-dedicated
branch in an intermediate decoder layer based on an original architecture of a U-shaped
image segmentation model, building and forming a knowledge
distillation frame, and selectively transferring task-
specific knowledge to obtain a knowledge distillation-dedicated
branch; selective distillation between the
encoder and the decoder is realized; and constructing a residual fusion selection module, integrating the characteristics of different distillation special branches, fusing the multi-stage student characteristics based on a residual fusion selection mode, matching the dimension of the teacher characteristics, and transferring the cross-stage characteristics from the teacher network to the student network. The method aims at a novel distillation framework of a U-shaped architecture model, focuses on selective distillation of
encoder or decoder features, enhances the efficiency and performance of the U-shaped model in tasks such as image segmentation and medical
image analysis by selectively transferring
specific knowledge of tasks, has excellent performance, and reduces calculation complexity.