Authors: Rita Buono, Elisabetta Cagni, Roberta Castriconi, Surendra Bahadur Chand, Marco Esposito, Claudio Fiorino, Valeria Landoni, Aldo Mazzilli, Eugenia Moretti, Lorenzo Placidi, Giulia Rambaldi Guidasci, Alessia Tudda
Affiliation: IRCCS San Raffaele Scientific Institute, Department of Advanced Technology, IRCCS Regina Elena National Cancer Institute, ASU FC Medical Physics, University Hospital of Parma AOUP, ICTP, B.P. Koirala Memorial Cancer Hospital, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Fatebenefratelli Isola Tiberina – Gemelli Isola
Abstract Preview: Purpose: To explore the feasibility and educational impact of transferring knowledge-based planning (KBP) models—developed using Italian breast radiotherapy data—to a Nepalese hospital, thereby demons...
Authors: Marissa Brown, Geoffrey D. Clarke, Luke Norton
Affiliation: University of Texas Health Science Center at San Antonio
Abstract Preview: Purpose: To evaluate how different learning strategies affect convolutional neural network (CNN) estimates of the liver's intravoxel incoherent motion (IVIM) parameters.
Methods: A 3-stage U-Net wa...
Authors: Jiali Gong, Yi Guo, Chi Han, Wanli Huo, Hongdong Liu, Zhao Peng, Yaping Qi, Zhaojuan Zhang
Affiliation: Department of Radiotherapy, cancer center, The First Affiliated Hospital of Fujian Medical University, Department of Oncology, Xiangya Hospital, Central South University, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, China Jiliang University, Division of lonizing Radiation Metrology, National Institute of Metrology, Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, the Zhejiang-New Zealand Joint Vision-Based Intelligent Metrology Laboratory, College of Information Engineering, China Jiliang University
Abstract Preview: Purpose: To address overfitting from limited training data in multi-organ segmentation, an efficient transfer learning framework is proposed. It reduces reliance on training samples, enabling a single...