GANs2023: GANs for Data Augmentation in Healthcare Sector |
Website | https://sites.google.com/view/gans-for-data-augmentation/home |
Submission link | https://easychair.org/conferences/?conf=gans2023 |
Abstract registration deadline | February 25, 2023 |
Submission deadline | March 30, 2023 |
Call for Book Chapter : GANs for Data Augmentation in Healthcare Sector
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
Submission Guidelines
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.
Inquires can be forwarded to:
- Dr. Arun Solanki | Email: asolanki@gbu.ac.in
- Dr. Mohd Naved | Email: mohdnaved@gmail.com ; mnaved@amity.edu
List of Topics
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Conditional GAN based Data Augmentation
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WGAN for Data Augmentation
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Augmentation of Chest Radiograph images (CXR) using GAN
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Data Augmentation approaches using cycle-consistent adversarial Network
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Segmentation for Unannotated Images
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GAN for Augmenting Cardiac MRI Segmentation
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Multi-stage Generative Adversarial Network for Segmentation
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Retinal vasculature segmentation
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High-resolution skin lesion synthesis
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Geometric transformations-based augmentation
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Color space Transformations with GAN.
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Unconditional Medical Image Synthesis
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Domain Transformation
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Conditional Image Synthesis
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Semi Supervised Based Annotation Sharing Augmentation
Editors
Dr. Arun Solanki
- Gautam Buddha University, Greater Noida, India.
- Email : asolanki@gbu.ac.in
Dr. Mohd Naved
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AIBS, Amity University, Noida, India.
Publication
This book will be published by Springer Nature.
Note : There are no submission or acceptance fees for manuscripts submitted to this book publication. All manuscripts are accepted based on a double-blind peer review editorial process.
Contact
Dr. Arun Solanki | Email: asolanki@gbu.ac.in
Dr. Mohd Naved | Email: mohdnaved@gmail.com ; mnaved@amity.edu