posted by user: lixt || 944 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

Federated Learning in IOT Cybersecurity 2021   PeerJ Computer Science - Federated Learning for Cybersecurity in Internet of Things
ICADCML 2022   3rd International Conference on Advances in Distributed Computing and Machine Learning - 2022
XSA 2021   Explainable Deep Learning for Sentiment Analysis
DL-ASAP 2022   Pattern Recognition Letters - Deep Learning for Acoustic Sensor Array Processing
DLIS 2022   Deep Learning for IoT Security - Frontiers in Big Data Journal
ICMLA 2021   20th IEEE International Conference on Machine Learning and Applications
CFDSP 2022   2022 International Conference on Frontiers of Digital Signal Processing (CFDSP 2022)
MLDM 2022   18th International Conference on Machine Learning and Data Mining
FAIML 2022   2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2022)
ICLR 2022   The Tenth International Conference on Learning Representations