Automated identification of clinical features from sparsely annotated 3-dimensional medical imaging.

ABSTRACT: One of the core challenges in applying machine learning and artificial intelligence to medicine is the limited availability of annotated medical data. Unlike in other applications of machine learning, where an abundance of labeled data is available, the labeling and annotation of medical data and images require a major effort of manual work by expert clinicians who do not have the time to annotate manually. In this work, we propose a new deep learning technique (SLIVER-net), to predict clinical features from 3-dimensional volumes using a limited number of manually annotated examples. SLIVER-net is based on transfer learning, where we borrow information about the structure and parameters of the network from publicly available large datasets. Since public volume data are scarce, we use 2D im

SUBMITTER: Rakocz N 

PROVIDER: S-EPMC7940637 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature