BBTác giả: Hussein, S.; Người hướng dẫn: -; Người tham gia: Kandel, P.; Bolan, C. W.; Wallace, M. B.; Bagci, U. (2019)
Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with
computer-aided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, prognosis, and foster personalized treatment planning as a part of precision medicine. In this papet, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Our first approach is based on supervised learning for which we demonstrate significant gains with deep learning algorithms, particularly by utilizing a 3D convolutional neural network and transfer learning. Motivated by the radiologists’ interpretation... 
