DAMI 2023 : Data-driven approaches to Medical Imaging
Call For Papers
We are editing a book on "Data-driven approaches to Medical Imaging", which will be published by Springer in February 2023.
The idea is that the combined chapters should reflect state of the art on the topic. Given your work on data science, computer vision, biomedical, medical imagaing we would like to invite you to contribute a chapter.
* The chapter can be a survey/review from some of your own work (i.e., we do not aim for original contributions).
* Feel free to pick co-authors.
*This is invitation only (we preferred extension of already published conference paper or summary of published journal papers)
There will be no competitive reviewing - although we will do a light reviewing round for feedback and improvements.
* Target is 20 pages (single column) per chapter, but we should be somewhat flexible
(let us know if you think you may have to deviate substantially from
* Deadline for chapters is 30th of Jan, 2023.
We would be very happy if you would contribute a chapter.
• Topic 1:. Introduction of Medical Imaging Modalities.
This section will cover the overall overview of. X-ray imaging and computed tomography, MRI and magnetic resonance microscopy, Nuclear imaging, Ultrasound imaging, Electrical Impedance Tomography (EIT), Emerging technologies for in vivo imaging, Contrast-enhanced MRI, MR approaches for osteoarthritis and cardiovascular imaging, Medical imaging data mining and search
• Topic-2. Introduction of Medical Imaging Informatics. This section will cover the basic concept of medical imaging informatics to discuss the basic knowledge of image processing, feature engineering and machine learning including the recent advancements in computer vision and different deep learning technologies to develop new quantitative image markers
or prediction models for disease detection, diagnosis and prognosis prediction.
• Topic-3. Active learning on medical images.
One of the biggest challenges in train machine learning models of medical images is lack of large image datasets particularly with the annotated image samples. This chapter will discuss the how to develop machine learning models of medical images when labeled or annotated data are rare or low. A case study with MRI will be presented step-by-step and discussed.
• Topic-4: Few shot learning on medical imaging . This section will discuss the medical imaging technologies when disease in few shot learning environment. This chapter will contain different casestudies.
• Topic-5: AutoML systems for medical imaging. This chapter will highlight all possible ways, approaches, and methods of computer vision can applied for medical imaging by leveraging power of AutoML.
• Topic-6: Online learning for X-ray, CT or MRI. This chapter will discuss the possible novel approaches of powering various online learning technique can work with ultrasound imaging
• Topic 7: Invariant Scattering Transform for Medical Imaging .it introduces a new area of research that merges the signal processing with deep learning for computer vision.
• Topic-8: Generative Adversarial Networks for Data Augmentation. This should discuss the problem of data scarcity and how GANs and various variational auto-encoders (VAEs) may help in augmenting data.
• Topic-9: Bias, Ethics and Explainability In Medical Imaging . This sections should discuss the Bias, Ethical concerns and explainable decision-making in Medical Imaging research. Medical Applications with deep learning networks
• Topic-10: Case studies on X-ray imaging ,MRI Nuclear imaging will be discussed.
PLEASE LET US KNOW BY January 20, 2023 if you are able to do so.
Also, please provide a tentative chapter title and author list for our planning. Latex template: Springer Chapter Template: https://drive.google.com/file/d/1MkZSuhlTFYUlJswhtUAa1kpkuNdJ7ket/view?usp=sharing.
It would be great to have you on board! Any questions - just let me know. Thank you.
Send inquiry or paper to firstname.lastname@example.org
Chapter Template: https://drive.google.com/file/d/1MkZSuhlTFYUlJswhtUAa1kpkuNdJ7ket/view?usp=sharing
• Dr. Bin Zheng:
Gerald Tuma Presidential Professor of School of Electrical and Computer Engineering
Director of Oklahoma Center of Medical Imaging for Translational Cancer Research (COBRE)
The University of Oklahoma
• Dr. Stefan Andrei, Professor.
Computer Science Department, Lamar
University, Texas, USA
• Dr. Md Kamruzzaman Sarker, Assistant Professor
Department of Computing Sciences, University of Hartford, Connecticut, USA
• Dr. Kishor Datta Gupta, , Assistant Professor
Cyber-Physical Department, Clark Atlanta University,