Book:DLMAUD-2020: Deep Learning for Medical Applications with Unique Data (Elsevier) |
Abstract registration deadline | June 19, 2020 |
Submission deadline | September 30, 2020 |
Call for Chapters
Deep Learning for Medical Applications with Unique Data
Edited by Deepak Gupta, Utku Kose, Ashish Khanna, Valentina Emilia Balas
to be published by Elsevier
Deep Learning is currently in a rapid rise for real world applications. Because of its advantages on dealing with big, complex amount of data, Deep Learning is applied in many fields and as a critical one, the field of medical has a remarkable interest in use of that sub-field of Artificial Intelligence. Thanks to use of Deep Learning techniques, many improvements have been done in terms of medical data analysis, diagnosis, treatment, and even personal healthcare. There are already many positive results provided by Deep Learning, in the literature of medical. For example, MIT has developed a system, which can detect breast cancer five years before its actual appearance and especially medical image analysis can be achieved with high accurateness thanks to Deep Learning. So, there is a great interest in resources focused on using Deep Learning for medical applications.
The main goal of this edited book: ‘Deep Learning for Medical Applications with Unique Data’ is to inform the target audience about the most recent Deep Learning based medical applications in which only unique data gathered generally in real cases is used. Moving from that goal, the other sub-goals of the book are can be mentioned as follows: (1) Enabling the audience to have information about how Deep Learning can be used in different problem scopes of medical. (2) Informing the audience with not only positive findings but also negative findings obtained by Deep Learning techniques. (3) Including the use of newly developed Deep Learning techniques reported rarely for now in the literature. (4) Excluding research works with ready data sets and including only unique data use for better understanding the state of Deep Learning in real cases experiences internationally. (5) Including also feedback / user experiences by physicians and/or medical staff for applied Deep Learning based solutions. (6) Focusing on popular medical application types of Deep Learning reported in the associated literature widely. (7) Opening minds for understanding current state and deriving ideas for the future state of medical applications with Deep Learning.
The target audience currently looks for more idea about alternative applications of Deep Learning, by considering its alternative techniques, alternative findings with different perspectives, and also results of using unique data. Such information can be taken from real implementations of Deep Learning for medical problems in different sides of the world.
Submission Guidelines
All submissions should be done by email to: utkukose@gmail.com and / or deepakgupta@mait.ac.in
All papers must be original and not simultaneously submitted to another book project, journal or conference. The following important dates will be considered for the submissions:
- Abstract - Proposal Submission: 19.06.2020
- Full Paper Submission: 30.09.2020
- (you may ask to utkukose@gmail.com for pre-proposal and full chapter preparation documents. Please track this page for further updates regarding submission procedures.)
List of Topics (as not limited to)
As considering Deep Learning and unique data;
- Medical image analysis,
- Medical signal analysis,
- Medical data synthesis,
- Hybrid solutions,
- Disease diagnosis,
- Patient care and treatment,
- Genomics studies,
- Robotic-autonomous solutions,
- Medical data pre-processing.
- ...etc.
Desired Main Topics for the Flow of the Table of Contents:
- Medical Data Analysis and Processing with Deep Learning: This part will include use of Deep Learning for analysis over different types of medical, unique data. Essential research trends in this manner will be medical image analysis, medical image registration, medical signal / time series analysis, medical data synthesis – information discovery (i.e. drug discovery), pre-processing operations…etc.
- Hybrid Solutions with the Deep Learning Support: This part will include developed hybrid solutions including Deep Learning and other solutions ways (i.e. other Artificial Intelligence solutions or traditional solutions) for specific medical applications considering unique data.
- Medical Diseases Diagnosis with Deep Learning: As among the trendiest topics, this part will include use of medical disease diagnosis done by Deep Learning based systems. More consideration will be given to especially rare diseases, and cancer. Data used for diseases will be unique.
- Deep Learning Applications for Patient Care and Treatment: This part will include works in which more consideration was given to patient care and treatment stages. Applications will include unique cases – data use.
- Deep Learning for Genomics: This part will include works focused especially use of Deep Learning for the research on Genomics. The data used will be unique.
- Deep Learning for Robotics and Autonomous Systems: This part will include mostly Deep Learning supported unique medical robotic systems and also developed unique physical devices having autonomous behaviors in terms of medical operations.
Editors
- Deepak Gupta, PhD. (Maharaja Agrasen Institute of Technology, India)
- Utku Kose, PhD. (Suleyman Demirel University, Turkey)
- Ashish Khanna, PhD. (Maharaja Agrasen Institute of Technology, India)
- Valentina Emilia Balas, PhD. (Aurel Vlaicu University of Arad, Romania)
Publication
'Deep Learning for Medical Applications with Unique Data' will be published by Elsevier.
Contact
All questions about submissions should be emailed to: utkukose@gmail.com