Machine Learning in Radiology 機器學習於醫學影像之發展
盧家鋒教授國立陽明大學生物醫學影像暨放射科學系 3:30 p.m. – 5:00 p.m. Hongjing Hall M107Abstract：
In this talk, we will review the
current development and applications of artificial intelligence, specifically
the machine-learning approach, in radiology. Machine-learning techniques have
shown promising benefits in the image acquisition, lesion detection,
differential diagnosis, tumor grading, and outcome prediction. In the field of
cancer imaging, growing evidence further reveals the feasibility of using image
phenotypes to probe the underlying genotypes and predict the clinical outcomes
using machine learning. We will start by introducing the common categories of
machine learning, including traditional approaches and deep learning, and
elucidating their usages for the different clinical purposes. The application
of machine learning combined with radiomics, a high-throughput feature
extraction method, to predict the molecular subtypes of lesion and patient
survivals based on multimodal MRI will be introduced. Finally, perspectives of
current limitations and future developments of machine learning in radiology
will be proposed and discussed to facilitate the machine-learning based
precision medicine.