3D Nodule Content-Based Metric Learning for Evidence-Based Lung Cancer Screening

Xiaoxi Lu1; Xingyue Wang1; Jiansheng Fang2; Na Zeng1; Jingqi Huang1; Chuangguang Huang3; Jingfeng Zhang4; Jianjun Zheng4; Heng Meng5; Jiang Liu1

10.1109/ICME57554.2024.10687628

2024-07-19

1945-7871

979-8-3503-9016-2

2024 IEEE International Conference on Multimedia and Expo (ICME)

15-19 July 2024

Niagara Falls, ON, Canada

The characteristics of 3D nodules on Computed Tomography (CT), including size, location, shape, and attenuation, are primary medical clues for distinguishing between benign and malignant nodules. To support evidence-based decision-making for lung cancer screening in clinical practice, we present a 3D Nodule Content-based Metric Learning (3D-NCML) network to retrieve subsolid-benign, subsolid-malignant, solid-benign, and solid-malignant nodules similar to the indeterminate ones. The inputs of 3D-NCML are 3D patches that exactly contain the whole nodule to ensure all visual information is included. A spatial position and size coding module, a shape encoder module, and an attenuation extraction module are designed based on medical clues for guiding the network to learn important characteristics of nodules. Experiments on the LIDC-IDRI dataset and a private dataset demonstrate that 3D-NCML outperforms other methods by quantitative and qualitative analysis, with more similar nodules retrieved and ranked ahead.

第一

1.Department of Computer Science and Engineering, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
2.Research and Development Institute, Guangzhou Guangri Stock Co., Ltd., Guangzhou, China
3.China Telecom Cloud Computing Corporation, Beijing, China
4.Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
5.Department of Neurology, The First Affiliated Hospital and Clinical Neuroscience Institute of Jinan University, Guangzhou, China

Xiaoxi Lu,Xingyue Wang,Jiansheng Fang,et al. 3D Nodule Content-Based Metric Learning for Evidence-Based Lung Cancer Screening[C],2024.