posted by user: lixt || 913 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

Bio-inspired Deep Learning 2021   CFP: Bio-inspired Deep Learning Image and Signal Processing Pipelines in Medical Oncology - PeerJ
ML_BDA 2021   Special Issue on Machine Learning Technologies for Big Data Analytics
Identifying Deep Fakes 2021   Deep Learning Algorithms and Techniques to Identify Deepfakes
MDPI mathematics 2021   MDPI mathematics - Special Issue on Computational Optimizations for Machine Learning
ASPAI 2021   3rd International Conference on Advances in Signal Processing and Artificial Intelligence
KER SI on EvoML 2021   Special Issue on Evolutionary Machine Learning in The Knowledge Engineering Review
ASPAI 2021   3rd International Conference on Advances in Signal Processing and Artificial Intelligence
Call for Book Chapter 2021   Handbook of DeepFakes and Face Manipulations - Call for Chapters
ICMLA 2021   20th IEEE International Conference on Machine Learning and Applications
WSDM 2022   Web Search and Data Mining