Book Title:
Advances in Deep Learning for Biomedical Engineering (DeepBiomed 2021)
Editors:
- Dr. Ripon Patgiri, National Institute of Technology Silchar, India (Email: ripon@cse.nits.ac.in)
- Dr. R. Murugan, National Institute of Technology Silchar, India (Email: murugan.rmn@ece.nits.ac.in)
- Dr. Tripti Goel, National Institute of Technology Silchar, India (Email: triptigoel@ece.nits.ac.in)
- Prof. Valentina Emilia Balas, Aurel Vlaicu University of Arad, Romania, (Email: balas@drbalas.ro)
About the Book:
Advances in Deep Learning for Biomedical Engineering (DeepBiomed 2021) is an edited book (multi-authored book) to be published at Advances in Computer Vision and Pattern Recognition, Springer book series (Approval Awaited). Nowadays, Deep Learning (DL) is the most demanded AI technology for diverse domains, for instance, image processing. Due to the advent of various DL algorithms, it is possible to detect, predict, and prevent various diseases and save many lives. In this book, we aim to cover various DL techniques used in Biomedical studies. The DL techniques are broadly categorized into four categories, namely, Convolution Neural Network, Deep Neural Network, Recurrent Neural Network and Deep Belief Network. These techniques are used to solve various unsolved challenges in Biomedical studies. For instance, DL can boost up the computational accuracy of cancer prediction and prevent the cancer in early stages. Moreover, this book explores transfer learning, extreme learning machines, federated learning and explainability for Biomedical studies. The DL techniques can be applied to numerous research areas of Biomedical research. Apart from core DL techniques, this book aims to cover transfer learning, federated learning and explainability to solve various issues of Health Informatics. The following key topics are covered in this book-
- Deep Learning: CNN, DNN, Evolutionary DNN, RNN, DBN, Transfer Learning, Federated Learning, and Explainability for various Biomedical Engineering, for instance, Cancers: lung cancer, oral cancer, bladder cancer, breast cancer, brain tumor, etc.
- Deep Learning for Biomedical Image Processing
- Deep Learning for Biomedical Signal Processing.
- New Deep Learning techniques for Biomedical Engineering.
Topics include, but not limited to…
Deep Learning techniques used in following topics but not limited to:
- Cancers: lung cancer, oral cancer, bladder cancer, breast cancer, brain tumor, Melanoma, Carcinoma, Sarcoma, Colon, etc.
- Extreme Learning in Biomedical Engineering
- Federated Learning in Biomedical Engineering
- EEG, ECG, EMG, EOG, EGG, and ERG Signals
- CT scans, tomography, Endoscopy, ultrasound, X-Ray, and Elastography
- MRI images
- 3D Biomedical Image processing
- Genomics and Protein-protein interaction
- Drug Discovery
- Internet of Medical Things
- Case Studies: COVID-19, and any other diseases
- Opportunities and Future Directions
Tentative Schedule and Submission Link
| Abstract Submission: | 1 December, 2021 |
| Abstract Notification: | 25 December, 2021 |
| Full Chapter Submission: | 25 March, 2022 |
| Full Chapter Notification: | 30 March, 2022 |
| Submission link: | https://easychair.org/conferen |
Submission Guidelines
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 1st December 2021.
- Chapters should be formatted using Springer overleaf template https://www.overleaf.com/latex/templates/springer-book-chapter/hrdcrfynnzjn
Publication
All accepted book chapters will be published in Advances in Computer Vision and Pattern Recognition, 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.
