About the Book
Computer-assisted diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an important technology in the medical industry, improving the accuracy of diagnostics. However, the lack Magnetic Resonance Imaging (MRI) data leads to the failure of the depth study algorithm. Medical records often different because of the cost of obtaining information and the time-consuming information. In general, clinical data are unreliable, the training of neural network methods to distribute disease across classes does not yield the desired results. Data augmentation is often done by training data to solve problems caused by augmentation tasks such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue.
Data Augmentation and Segmentation imaging using GAN can be used to provide clear images of brain, liver, chest, abdomen, and liver on MRI. In addition, GAN shows strong promise in the field of clinical image synthesis. In many cases, clinical evaluation is limited by a lack of data and/or the cost of actual information. GAN can overcome these problems by enabling scientists and clinicians to work on beautiful and realistic images. This can improve diagnosis, prognosis, and disease. Finally, GAN highlights the potential for location of patient information with data. This is a beneficial clinical application of GAN because it can effectively protect patient confidentiality. The proposed book will cover the application of GANs on medical imaging augmentation and segmentation
Tentative List of Chapters
- Conditional GAN based Data Augmentation
- WGAN for Data Augmentation
- Augmentation of Chest Radiograph images (CXR) using GAN
- Data Augmentation approaches using cycle-consistent adversarial Network
- Segmentation for Unannotated Images
- GAN for Augmenting Cardiac MRI Segmentation
- Multi-stage Generative Adversarial Network for Segmentation
- Retinal vasculature segmentation
- High-resolution skin lesion synthesis
- Geometric transformations-based augmentation
- Color space Transformations with GAN.
- Unconditional Medical Image Synthesis
- Domain Transformation
- Conditional Image Synthesis
- Semi Supervised Based Annotation Sharing Augmentation
EDITORS
Important Dates
Abstract Submission : 25th Jan. 2023
Abstract Acceptance : 10th Feb. 2023
Full Chapter Submission : 25th Feb. 2023
First Decision/Corrections : 10th March. 2023
Final Chapter Due : 20th March. 2023
Submission Process
All the contributors are invited to submit their proposal in file specifying:
Proposed/Tentative Title of the Chapter
List of contributing co-authors (Specify affiliation for each coauthor)
Abstract- 300 words
6 Keywords
Table of Contents/Tentative Structure of the Chapter.
The Submission for the Proposal is welcome till 25th Jan. 2023 via following EasyChair link.
https://easychair.org/conferences/?conf=gans2023
Inquires can be forwarded to:
Dr. Arun Solanki | Email: asolanki@gbu.ac.in
Dr. Mohd Naved | Email: mohdnaved@gmail.com ; mnaved@amity.edu