面向缺失模态医学分割的原型知识蒸馏
Prototype Knowledge Distillation for Medical Segmentation with Missing Modality
AI总结：
本文提出原型知识蒸馏方法ProtoKD，通过将多模态的像素级知识及类内类间特征变化迁移到单模态学生模型，解决医学分割中的缺失模态问题，并在BraTS上取得最优性能。
摘要：
多模态医学成像在临床治疗中至关重要，因为它能为医学图像分割提供互补信息。然而，由于扫描时间和其他临床情况的限制，在临床中收集多模态数据较为困难。因此，开发一种能够处理这一缺失模态问题的图像分割范式具有重要的临床意义。本文提出了一种原型知识蒸馏（ProtoKD）方法来解决这一具有挑战性的问题，尤其是针对只能获取单模态数据这一最困难的情形。具体而言，我们的ProtoKD不仅能够将多模态数据的逐像素知识蒸馏到单模态数据中，还能传递类内和类间特征变化，从而使学生模型能够从教师模型中学习到更鲁棒的特征表示，并仅利用单模态数据进行推理。我们的方法在BraTS基准上取得了最先进的性能。代码可在\url{https://github.com/SakurajimaMaiii/ProtoKD}获取。
英文摘要：
Multi-modality medical imaging is crucial in clinical treatment as it can provide complementary information for medical image segmentation. However, collecting multi-modal data in clinical is difficult due to the limitation of the scan time and other clinical situations. As such, it is clinically meaningful to develop an image segmentation paradigm to handle this missing modality problem. In this paper, we propose a prototype knowledge distillation (ProtoKD) method to tackle the challenging problem, especially for the toughest scenario when only single modal data can be accessed. Specifically, our ProtoKD can not only distillate the pixel-wise knowledge of multi-modality data to single-modality data but also transfer intra-class and inter-class feature variations, such that the student model could learn more robust feature representation from the teacher model and inference with only one single modality data. Our method achieves state-of-the-art performance on BraTS benchmark. The code is available at \url{https://github.com/SakurajimaMaiii/ProtoKD}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
