Authors:
Jason Hagerty
;
R. Joe Stanley
and
William V. Stoecker
Affiliation:
Missouri University of Science and Technology, United States
Keyword(s):
Deep Learning, Convolution Neural Networks, Fusion, Transfer Learning.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Image Generation Pipeline: Algorithms and Techniques
;
Medical Image Applications
Abstract:
Deep learning, in particular convolutional neural networks, has increasingly been applied to medical
images. Advances in hardware coupled with availability of increasingly large data sets have fueled this rise.
Results have shattered expectations. But it would be premature to cast aside conventional machine learning
and image processing techniques. All that deep learning comes at a cost, the need for very large datasets.
We discuss the role of conventional manually tuned features combined with deep learning. This process of
fusing conventional image processing techniques with deep learning can yield results that are superior to
those obtained by either learning method in isolation. In this article, we review the rise of deep learning in
medical image and the recent onset of fusion of learning methods. We discuss supervision equilibrium point
and the factors that favor the role of fusion methods for histopathology and quasi-histopathology modalities.