A multi-Task Learning based applicable AI model simultaneously predicts stage, histology, grade and LNM for cervical cancer before surgery

• Main Authors: Zhixiang Wang (Author), Huiqiao Gao (Author), Xinghao Wang (Author), Marcin Grzegorzek (Author), Jinfeng Li (Author), Hengzi Sun (Author), Yidi Ma (Author), Xuefang Zhang (Author), Zhen Zhang (Author), Andre Dekker (Author), Alberto Traverso (Author), Zhenyu Zhang (Author), Linxue Qian (Author), Meizhu Xiao (Author), Ying Feng (Author)
• Formato: Libro
• Publicado: BMC, 2024-07-01T00:00:00Z.
• Subjects: article
• Acceso en liña: Connect to this object online.

Abstract Purpose To build an Mult-Task Learning (MTL) based Artificial Intelligence(AI) model that can simultaneously predict clinical stage, histology, grade and LNM for cervical cancer before surgery. Methods This retrospective and prospective cohort study was conducted from January 2001 to March...