深度学习
计算机科学
人工智能
无线电技术
串联(数学)
特征工程
特征(语言学)
机器学习
特征选择
模式识别(心理学)
任务(项目管理)
特征提取
心房颤动
人工神经网络
卷积神经网络
特征学习
多任务学习
医学
语言学
哲学
数学
管理
心脏病学
组合数学
经济
作者
Weihang Dai,Xiaomeng Li,Taihui Yu,Dongdong Zhao,Jun Shen,Kwang-Ting Cheng
标识
DOI:10.1007/978-3-031-43990-2_15
摘要
Atrial Fibrillation (AF) is characterized by rapid, irregular heartbeats, and can lead to fatal complications such as heart failure. The disease is divided into two sub-types based on severity, which can be automatically classified through CT volumes for disease screening of severe cases. However, existing classification approaches rely on generic radiomic features that may not be optimal for the task, whilst deep learning methods tend to over-fit to the high-dimensional volume inputs. In this work, we propose a novel radiomics-informed deep-learning method, RIDL, that combines the advantages of deep learning and radiomic approaches to improve AF sub-type classification. Unlike existing hybrid techniques that mostly rely on naïve feature concatenation, we observe that radiomic feature selection methods can serve as an information prior, and propose supplementing low-level deep neural network (DNN) features with locally computed radiomic features. This reduces DNN over-fitting and allows local variations between radiomic features to be better captured. Furthermore, we ensure complementary information is learned by deep and radiomic features by designing a novel feature de-correlation loss. Combined, our method addresses the limitations of deep learning and radiomic approaches and outperforms state-of-the-art radiomic, deep learning, and hybrid approaches, achieving 86.9% AUC for the AF sub-type classification task. Code is available at https://github.com/xmed-lab/RIDL .
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