论文标题
MED-TEX：通过预验证的医学成像模型的数据转移和解释知识
MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models
论文作者
论文摘要
深度学习方法通常需要大量的培训数据，并且缺乏解释性。在本文中，我们提出了一种新颖的知识蒸馏和医学图像分类的模型解释框架，该框架共同解决了上述两个问题。具体来说，为了解决渴望数据的问题，通过从繁琐的熟悉的教师模型中提取知识来学习一个小的学生模型。为了解释教师模型并协助学习学生的学习，引入了解释器模块，以强调输入区域，这对于教师模型的预测很重要。此外，该联合框架是通过从信息理论的角度衍生出的原则性方式来训练的。与Fellus数据集上的最新方法相比，我们的框架在知识蒸馏和模型解释任务方面的表现优于知识蒸馏和模型解释任务。
Deep learning methods usually require a large amount of training data and lack interpretability. In this paper, we propose a novel knowledge distillation and model interpretation framework for medical image classification that jointly solves the above two issues. Specifically, to address the data-hungry issue, a small student model is learned with less data by distilling knowledge from a cumbersome pretrained teacher model. To interpret the teacher model and assist the learning of the student, an explainer module is introduced to highlight the regions of an input that are important for the predictions of the teacher model. Furthermore, the joint framework is trained by a principled way derived from the information-theoretic perspective. Our framework outperforms on the knowledge distillation and model interpretation tasks compared to state-of-the-art methods on a fundus dataset.
