DL-empowered cancer imaging Call for Papers

We've organized a 
SI in IET Image processing
DL-empowered cancer imaging
https://ietresearch.onlinelibrary.wiley.com/hub/journal/17519667/homepage/cfp

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Cancer presents a unique circumstance for medical decisions due to not
only its various types of disease growth, but also the requirement for
early, fast and proper detection of the individual patientís
condition, their capability to receive treatment, and their responses
to treatment. Despite advances in technology, correct detection,
categorization, and monitoring of cancers remains a challenge. The
majority of radiological disease analysis is based on visual
examinations, which can be supplemented by intelligent computing
techniques. Deep Learning (DL) approaches have the potential to bring
about significant advances in the analysis and interpretation of
cancer images by medical experts. These include prediction of cancer
susceptibility, prediction of cancer recurrence, prediction of the
stage and grade of cancer, tracking tumor development, etc. Proper
monitoring of the impact of the disease and the corresponding
treatment on surrounding tissues is another big challenge in the case
of a cancer diagnosis. DL has the potential to automate image
interpretation procedures, the clinical workflow of radiological
detection, and management decisions on whether or not to administer an
intervention.

This Special Issue aims to publish the latest developments in research
on all facets of DL-empowered cancer imaging. This special issue
especially welcomes submissions that depict the end-to-end
technological viewpoint that uses automated informatics systems to
solve single or multiple cases of healthcare advancements based on
cancer imaging. Papers describing new deep learning algorithms based
on cancer imaging are especially welcome. The papers will be chosen
based on their scientific merit, contribution to the field of deep
learning-based image processing, and importance to cancer detection
and diagnosis. To establish the effectiveness of any proposed
approach, authors should use the relevant cancer imaging datasets.

With Jyotismita Chaki as the Lead Guest Editor and Victor Albuquerque
and Marcin Wozniak as Guest Editors, submissions must be made through
ScholarOne by 28 February 2023. More information on this special issue
and submitting an article can be found here.

https://ietresearch.onlinelibrary.wiley.com/pb-assets/assets/17519667/Special%20Issues/IET_IPR_CFP_DLTCI-1653483641023.pdf