posted by user: lixt || 874 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

DLRS 2021   Call for Papers: Topical Issue on Deep Learning for Recommender Systems
SDLDIP 2021   Special Issue on Sensors and Deep Learning for Digital Image Processing
DELVIC 2020   [J. Imaging] Special Issue Deep Learning for Visual Contents Processing and Analysis
MLDM 2021   17th International Conference on Machine Learning and Data Mining
ICDM 2021   21th Industrial Conference on Data Mining
AICA 2020   O'Reilly AI Conference San Jose
14th ICTEL August, Barcelona 2021   14th ICTEL 2021 – International Conference on Teaching, Education & Learning, 23-24 August, Barcelona
EDL-AI 2020   Explainable Deep Learning- AI / ICPR'2020 WorkShop
SI-DAMLE 2020   Special Issue on Data Analytics and Machine Learning in Education
CEC 2021   IEEE Congress on Evolutionary Computation