Cost-Sensitive Uncertainty Hypergraph Learning for Identification of Lymph Node Involvement With CT Imaging

Frontiers in Medicine

article

Qianli Ma¹  Jielong Yan²  Jun Zhang³  Qiduo Yu¹  Yue Zhao¹  Chaoyang Liang¹  Donglin Di² 

[1] Department of Thoracic Surgery, China-Japan Friendship Hospital;The School of Software, Tsinghua University;Tencent AI Lab

关键词: lymph node involvement;    CT imaging;    hypergraph learning;    cost-sensitive;    lung cancer;   

DOI  :  10.3389/fmed.2022.840319

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Lung adenocarcinoma (LUAD) is the most common type of lung cancer. Accurate identification of lymph node (LN) involvement in patients with LUAD is crucial for prognosis and making decisions of the treatment strategy. CT imaging has been used as a tool to identify lymph node involvement. To tackle the shortage of high-quality data and improve the sensitivity of diagnosis, we propose a Cost-Sensitive Uncertainty Hypergraph Learning (CSUHL) model to identify the lymph node based on the CT images. We design a step named “Multi-Uncertainty Measurement” to measure the epistemic and the aleatoric uncertainty, respectively. Given the two types of attentional uncertainty weights, we further propose a cost-sensitive hypergraph learning to boost the sensitivity of diagnosing, targeting task-driven optimization of the clinical scenarios. Extensive qualitative and quantitative experiments on the real clinical dataset demonstrate our method is capable of accurately identifying the lymph node and outperforming state-of-the-art methods across the board.

【 授权许可】

CC BY