Computer-aided detection of pulmonary nodules in low-dose CT
P. Delogu, M. E. Fantacci, I. Gori, A. Preite Martinez, A. Retico
备注：
3 pages, 4 figures; Proceedings of the CompIMAGE - International Symposium on Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications, 20-21 Oct. 2006, Coimbra, Portugal
A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical CT images with 1.25 mm slice thickness is being developed in the framework of the INFN-supported MAGIC-5 Italian project. The basic modules of our lung-CAD system, a dot enhancement filter for nodule candidate selection and a voxel-based neural classifier for false-positive finding reduction, are described. Preliminary results obtained on the so-far collected database of lung CT scans are discussed.
Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules
Department of Radiation Oncology, University of Virginia School of Medicine（弗吉尼亚大学医学院放射肿瘤学系）；Department of Physics, University of Virginia（弗吉尼亚大学物理系）；Department of Physics, Massachusetts Institute of Technology（麻省理工学院物理系）；Department of Biomedical Engineering, Northwestern University（西北大学生物医学工程系）；Department of Radiation Oncology, University of Virginia（弗吉尼亚大学放射肿瘤学系）；Old Dominion University（旧 Dominion 大学）
Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss
The Hong Kong Polytechnic University（香港理工大学）；Sun Yat-Sen University（中山大学）；Fourth Military Medical University（中国人民解放军空军军医大学）；Xijing Hospital（西京医院）；Queen Mary Hospital（玛丽医院）；Guangzhou University of Chinese Medicine（广州中医药大学）；Soochow University（苏州大学）
