1 research outputs found
A Survey on Deep Learning of Small Sample in Biomedical Image Analysis
The success of deep learning has been witnessed as a promising technique for
computer-aided biomedical image analysis, due to end-to-end learning framework
and availability of large-scale labelled samples. However, in many cases of
biomedical image analysis, deep learning techniques suffer from the small
sample learning (SSL) dilemma caused mainly by lack of annotations. To be more
practical for biomedical image analysis, in this paper we survey the key SSL
techniques that help relieve the suffering of deep learning by combining with
the development of related techniques in computer vision applications. In order
to accelerate the clinical usage of biomedical image analysis based on deep
learning techniques, we intentionally expand this survey to include the
explanation methods for deep models that are important to clinical decision
making. We survey the key SSL techniques by dividing them into five categories:
(1) explanation techniques, (2) weakly supervised learning techniques, (3)
transfer learning techniques, (4) active learning techniques, and (5)
miscellaneous techniques involving data augmentation, domain knowledge,
traditional shallow methods and attention mechanism. These key techniques are
expected to effectively support the application of deep learning in clinical
biomedical image analysis, and furtherly improve the analysis performance,
especially when large-scale annotated samples are not available. We bulid demos
at https://github.com/PengyiZhang/MIADeepSSL