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 na\"ive 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.

翻译：房颤（AF）表现为快速、不规则的心跳，可能导致心力衰竭等致命并发症。该疾病根据严重程度分为两种亚型，可通过CT体数据自动分类以筛查重症病例。然而，现有分类方法依赖通用的放射组学特征，这些特征可能并非该任务的最优选择，而深度学习方法则容易对高维度体数据输入产生过拟合。在本研究中，我们提出一种创新的放射组学引导深度学习方法RIDL，该方法结合了深度学习与放射组学方法的优势，以改善房颤亚型分类。与多数依赖简单特征拼接的现有混合技术不同，我们观察到放射组学特征选择方法可作为信息先验，并建议用局部计算的放射组学特征补充浅层深度神经网络（DNN）特征。这既减少了DNN的过拟合，又使得放射组学特征之间的局部差异能被更好地捕捉。此外，我们通过设计一种新颖的特征去相关损失函数，确保深度特征与放射组学特征能学习互补信息。综合而言，我们的方法克服了深度学习与放射组学方法的局限性，在房颤亚型分类任务中实现了86.9%的AUC，性能超越现有最先进的放射组学、深度学习和混合方法。代码详见https://github.com/xmed-lab/RIDL。