CALL FOR CHAPTERS - Advances in Deep Generative Models for Medical Artificial Intelligence

[H. Ali]

Call for book chapters

Advances in Deep Generative Models for Medical Artificial Intelligence  

It is our pleasure to invite you to contribute a book chapter for our edited book entitled “Advances in Deep Generative Models for Medical Artificial Intelligence, which will be published by Springer Nature publishers in the series on Studies in Computational Intelligence (Scopus indexed). There is no publication fee.  

Deep generative models such as GANs, Autoencoders, and Neural Diffusion Models have the potential to learn the distribution of real data and augment the data by synthesis. While these models have been popular for generating synthetic medical image data, there has been an increasing interest in developing these models for other applications such as segmentation, diagnosis, and super-resolution. Besides, with the recent successful applications of diffusion models for synthesis of art images (the now famous DALL-E 2 framework that uses diffusion models), it is expected to gain more insights into medical image data. Consequently, there is a rapid influx of new architectures for applications in medical imaging and healthcare data.  

This book aims to achieve the objectives and present the concepts and applications to the readers in a comprehensive way. The book will highlight the recent developments in deep generative models such as Generative Adversarial Networks (GANs) and Neural Diffusion Models and their role in building Artificial Intelligence (AI) models for computer-aided diagnosis using medical multimedia data, medical image data, and clinical data.

Potential topics include (but not limited to):

1. Generative Adversarial Networks for MRI images.

2. Generative Adversarial Networks for medical image data augmentation

3. Deep generative models for precision medicine.

4. Deep generative models for disease modeling and prognosis.  

5. Deep generative models for domain-to-domain transformation in medical images.  

6. Deep learning for ultrasound images

7. Deep generative models with 3D architectures for ultrasound sequences.

8. Noise adaptation in medical images with deep generative models.

9. Vascular ultrasound image analysis using deep generative models.  

10. Deep generative models for domain adaptation and in-vivo to in-vitro transformation.  

11. Deep learning for skin cancer diagnosis

12. Deep learning on digital mammograms.  

13. computer-aided diagnosis, image segmentation, tissue recognition, and classification

14. Neural Diffusion Models for medical image synthesis.  

15. Neural Diffusion Models for noise adaptation in medical images.

16. Deep generative models on edge computing devices for computer-aided diagnosis.

Timeline:  

Abstract Submission          -  30 November 2022

Full Chapter submission    - 31 January 2023

Decision notification          - 31 March 2023

Revised submission           - 30 April 2023

Camera ready submission - 30 May 2023

More information:

More details are available on https://medicalai.weebly.com/

For any questions related to the book, please reach out to any member of the editorial team.  

Editors:  

Dr. Hazrat Ali, Hamad Bin Khalifa University, Qatar. 

Dr. Mubashir Husain Rehmani, Munster Technological University, Ireland

Dr. Zubair Shah, Hamad Bin Khalifa University, Qatar.