Deep Learning in Health Informatics: Theory, Practice and Applications (DeepHealth 2022)
- Dr. Ripon Patgiri, National Institute of Technology Silchar, India (Email: email@example.com)
- Dr. Anupam Biswas, National Institute of Technology Silchar, India (Email: firstname.lastname@example.org)
- Dr. Pinki Roy, National Institute of Technology Silchar, India (Email: email@example.com)
About the Book:
This book is proposed in successful completion of the “Deep Learning in Health Informatics: Theory, Practice and Applications (DeepHealth 2022)”, Springer, 2021, Edited book. In continuation, this book emphasizes a more narrow topic and focuses on Deep Learning in Health Informatics. Deep Learning in emerging technology that can correctly address most of the unsolved problems in the Health Informatics. Therefore, this book covers various emerging aspects of current research challenges and its solutions using Deep Learning technology. Deep Learning is applied in the broad area of health informatics, including diverse biomedical imaging, (2D biomedical imaging, 3D biomedical imaging, MRI), numerous biomedical signals (ECG, EEG & EMG), several cancer issues (lung cancer, breast cancers, colon cancers, etc.), and healthcare. Therefore, our book on “Deep Learning in Health Informatics: Theory, Practice and Applications (DeepHealth 2022)” aims at presenting diverse issues in health informatics as follows-
- Address emerging challenges and its solution using Deep Learning
- Focus on diverse biomedical issues for welfare of human-being
- Dissemination of recent findings on health informatics
Topics include, but not limited to…
Deep Learning techniques used in following topics but not limited to:
- Diverse Deep Learning architecture for Healthcare
- Deep Learning in Cancer
- Deep Learning for Smart Healthcare
- Deep Learning in Drug discovery
- Deep learning in Medical imaging
- Deep Learning in Insurance fraud
- Deep learning in Alzheimer’s disease
- Deep Learning in Genome
- Deep Learning in Electronic Health Records
- Generative Adversarial Network
- Deep Learning in Disease Prediction and Treatment
- Deep Learning in Diabetic Retinopathy
- Deep Learning in Human Immunodeficiency Virus
- Federated learning
- Machine Unlearning
- Issues, Challenges and Future Directions
- Case studies : Cancer, Diabetes, Flu etc.
Tentative Schedule and Submission Link
|Abstract Submission:||05 January, 2022|
|Abstract Notification:||15 January, 2022|
|Full Chapter Submission:||25 March, 2022|
|Full Chapter Notification:||30 March, 2022|
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:
- All chapters must be original and not simultaneously submitted to another book, journal or conference.
- Chapters should not exceed similarity index of 10% excluding references.
- Chapter length 15-25 pages.
- Author must propose their book chapter proposal within 5th January 2022.
- Chapters should be formatted using Springer overleaf template https://www.overleaf.com/latex/templates/springer-book-chapter/hrdcrfynnzjn
All accepted book chapters will be published in Studies in Computational Intelligence, Springer book series (Approval awaited). This series is indexed in SCOPUS, SCIMAGO, and zbMATH. All books published in the series are submitted for consideration in Web of Science.
No publication charge.