posted by user: lixt || 719 views || tracked by 1 users: [display]

EDL 2019 : Evolutionary Deep Learning in Cancer Diagnoses


When N/A
Where N/A
Abstract Registration Due Nov 16, 2018
Submission Deadline May 13, 2019

Call For Papers

Recently, much of the field of cancer diagnosis has been focused on developing new computational methods. However, most of these methods suffer from lower accuracy, experimental noise, high dimensionality, and poor interpretability. These methods still require significant improvement, so that can meet the need of real-world clinical diagnosis.

Machine learning algorithms have pushed the boundaries for numerous problems in areas such as computer vision, natural language processing, and audio processing. Recent cancer research has also focused on machine learning, which has attracted attention from both the academic research and commercial application communities. In a different yet often closely related arena, evolutionary algorithms use a population-based approach in which more than one solution participates in an iteration and evolves a new population of solutions in each iteration. Meanwhile, evolutionary algorithms have successfully been employed to increase the performance of machine learning methods significantly.

With this perspective, this Research Topic will collect cutting-edge research in all aspects of evolutionary algorithm and machine learning for cancer diagnoses including experimental and theoretical research and real-world applications to promote research, sharing, and development.

We welcome all types of articles accepted within the Bioinformatics and Computational Biology speciality section (please see here ). Potential topics include, but are not limited to the following:
• Deep learning for cancer diagnoses,
• Perspectives on evolutionary machine learning,
• Multiobjective cancer diagnoses,
• Mathematical modelling of cancer diagnoses,
• Conventional machine learning methods for cancer diagnoses
• Unsupervised cancer diagnoses

Keywords: Cancer Diagnoses, Evolutionary Algorithm, Multiobjective Optimization, Evolutionary Deep Learning, Evolutionary Machine Learning

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Related Resources

CMES_RADLMSA 2020   CMES_Recent Advances on Deep Learning for Medical Signal Analysis (IF: 0.796)
ETRIJ 5G&B5G 2020   ETRI Journal Special Issue on 5G & B5G Enabling Edge Computing, Big Data & Deep Learning Technologies
Journal Special Issue 2019   Machine Learning on Scientific Data and Information
ISBDAI 2020   【Ei Compendex Scopus】2018 International Symposium on Big Data and Artificial Intelligence
ICPR 2020   International Conference on Pattern Recognition 2020
CVPR 2020   Computer Vision and Pattern Recognition
IEEE COINS 2020   Internet of Things IoT | Artificial Intelligence | Machine Learning | Big Data | Blockchain | Edge & Cloud Computing | Security | Embedded Systems | Circuit and Systems | WSN | 5G
DATA 2020   AI and Big Data in Cancer: From Innovation to Impact
EWRE-EI/Scopus 2019   2019 2nd International Conference on Environmental and Water Resources Engineering
Data Science 2020   3nd Annual International Great Lakes Data Science Symposium