计算机科学
人工智能
无线电技术
卷积神经网络
机器学习
深度学习
模式识别(心理学)
任务(项目管理)
特征学习
经济
管理
作者
Ziteng Zhao,Guanyu Yang
标识
DOI:10.1007/978-3-030-87196-3_24
摘要
Tumor classification is important for decision support of precision medicine. Computer-aided diagnosis by convolutional neural networks relies on a large amount of annotated dataset, which is costly sometimes. To solve the poor predictive ability caused by tumor heterogeneity and inadequate labeled image data, a self-supervised learning method combined with radiomics is proposed to learn rich visual representation about tumors without human supervision. A self-supervised pretext task, namely “Radiomics-Deep Feature Correspondence”, is formulated to maximize agreement between radiomics view and deep learning view of the same sample in the latent space. The presented self-supervised model is evaluated on two public medical image datasets of thyroid nodule and kidney tumor and achieves high score on linear evaluations. Furthermore, fine-tuning the pre-trained network leads to a better score than the train-from-scratch models on the tumor classification task and shows label-efficient performance using small training datasets. This shows injecting radiomics prior knowledge about tumors into the representation space can build a more powerful self-supervised method.
科研通智能强力驱动
Strongly Powered by AbleSci AI