Reference #: BMS-CMIM-2021-HT-60
Proposal Title: Recent Advancement in Medical Imaging with Deep Learning for Health Care Applications
Summary:
Understanding, interpreting, and assessing medical images in clinical and healthcare applications rely heavily on medical image processing and analysis. Increasingly quick diagnosis and more precise treatment options have been made possible by technological advancements in the last two decades, increasing patient safety while decreasing processing time and cost. The advanced deep learning based computational methods are used to extract quantitative information from medical images. The availability of huge datasets has made current neural network based end-to-end machine learning and deep learning methods very successful and had a significant effect in many areas.
Medical image processing issues such as segmentation, visualisation, registration, and navigation may seem to be distinct, yet they are all intertwined in the process of resolving clinical bottlenecks. Using deep learning algorithms, researchers were able to achieve record-breaking performance and set the bar for future research. Due to the extensive quantity of medical imaging data of CT scan, ultrasound, and MRI, there is widespread use of machine learning, specifically deep learning, to discover specific patterns on such data. Such large data is well quantified by deep learning models. Deep learning is now being utilised, customised, and particularly developed for medical image analysis, as opposed to when it was first introduced to the community. Having learned more about the techniques, researchers have come up with innovative ideas for combining artificial intelligence (AI) with neural networks to solve difficult issues like medical image reconstruction. There are various applications of medical image analysis that include: medical image denoising, medical image super resolution, multi-modal image fusion, medical image registration, medical image segmentation, medical image super-resolution, diagnose abnormalities in medical images, medical image synthesis etc.
This special issue is meant to give an alertness of medical image processing and analysis and many deep learning algorithms to analyze medical data. It mainly focuses on major achievements and developments in medical imaging, clinical, and health care applications.
We invite submissions successfully applying unconventional deep learning algorithms to the real-time problems directly or indirectly addressing the medical images, and health care applications.
Dear Sir/Madam,
I am pleased to invite you to contribute to my thematic issue entitled "Recent Advancement in Medical Imaging with Deep Learning for Health Care Applications" in the "Current Medical Imaging". Please find below the submission link for the thematic issue.
https://bentham.
Sincerely,
Dr. Manoj Diwakar, Dr. Prabhishek Singh
manoj.diwakar@gmail.com, prabhisheksingh88@gmail.com
Current Medical Imaging