论文标题
CT中有效的半监督腹部器官分割的跨教学教师的知识蒸馏
Knowledge Distillation from Cross Teaching Teachers for Efficient Semi-Supervised Abdominal Organ Segmentation in CT
论文作者
论文摘要
有关医学图像分割的深度学习模型的更多临床应用，必须解决对标记的数据和计算资源的高需求。这项研究提出了一个通过两个教师模型和一个学生模型的粗到精细框架，该框架结合了知识蒸馏和交叉教学，这是基于伪标签的一致性正则化，以进行有效的半监督学习。在MICCAI Flare 2022挑战下，在CT图像中的腹部多器官分割任务上证明了该方法，分别在验证和测试集中的平均骰子得分分别为0.8429和0.8520。
For more clinical applications of deep learning models for medical image segmentation, high demands on labeled data and computational resources must be addressed. This study proposes a coarse-to-fine framework with two teacher models and a student model that combines knowledge distillation and cross teaching, a consistency regularization based on pseudo-labels, for efficient semi-supervised learning. The proposed method is demonstrated on the abdominal multi-organ segmentation task in CT images under the MICCAI FLARE 2022 challenge, with mean Dice scores of 0.8429 and 0.8520 in the validation and test sets, respectively.
