Medical Image Processing in the Age of Deep Learning - Is There Still Room for Conventional Medical Image Processing Techniques?

Jason Hagerty, R. Joe Stanley, William V. Stoecker

2017

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.

Paper Citation

in Harvard Style

Hagerty J., Stanley R. and Stoecker W. (2017). Medical Image Processing in the Age of Deep Learning - Is There Still Room for Conventional Medical Image Processing Techniques? . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 306-311. DOI: 10.5220/0006273803060311

in Bibtex Style

@conference{visapp17,
author={Jason Hagerty and R. Joe Stanley and William V. Stoecker},
title={Medical Image Processing in the Age of Deep Learning - Is There Still Room for Conventional Medical Image Processing Techniques?},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={306-311},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006273803060311},
isbn={978-989-758-225-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Medical Image Processing in the Age of Deep Learning - Is There Still Room for Conventional Medical Image Processing Techniques?
SN - 978-989-758-225-7
AU - Hagerty J.
AU - Stanley R.
AU - Stoecker W.
PY - 2017
SP - 306
EP - 311
DO - 10.5220/0006273803060311