Proceedings of the 3rd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2019)

Application of Deep Learning in Immunotherapy

Authors
Nannan Guo, Lichen Zhang
Corresponding Author
Nannan Guo
Available Online July 2019.
DOI
10.2991/iccia-19.2019.26How to use a DOI?
Keywords
Immunity; therapy; Deep learning.
Abstract

In recent years, the study of deep learning in medicine has developed rapidly. This article uses a variety of deep neural networks to explore the relationship between DNA-mutated tumors and immunotherapy. This article uses a variety of methods to image preprocess images. Such as: interest area (ROI) extraction, color standardization, cleaning dirty data, and so on. A variety of deep learning models (eg caffe, keras), a variety of neural network models Xception, ResNet50 for training. Finally, the greater the value of TMB associated with DNA mutations, the better the effect of immunotherapy.

Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 3rd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2019)
Series
Advances in Computer Science Research
Publication Date
July 2019
ISBN
10.2991/iccia-19.2019.26
ISSN
2352-538X
DOI
10.2991/iccia-19.2019.26How to use a DOI?
Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Nannan Guo
AU  - Lichen Zhang
PY  - 2019/07
DA  - 2019/07
TI  - Application of Deep Learning in Immunotherapy
BT  - Proceedings of the 3rd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2019)
PB  - Atlantis Press
SP  - 169
EP  - 175
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-19.2019.26
DO  - 10.2991/iccia-19.2019.26
ID  - Guo2019/07
ER  -