Deep Learning in Cancer Imaging: Developments and Future Prospects
A special issue of Tomography (ISSN 2379-139X). This special issue belongs to the section "Cancer Imaging".
Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 304
Special Issue Editor
Special Issue Information
Dear Colleagues,
Machine learning, a subbranch of artificial intelligence, is a technique used to recognize patterns from trained data. Machine learning has been applied in medical imaging, but the recent advancements in deep learning have gained the attention of experts both in academia and industry. As a subfield of machine learning, deep learning methods are now widespread in a variety of businesses and institutes such as health care worldwide. Likewise, the application of deep learning methods in cancer imaging is also accelerating, with the center of the application being cancer diagnosis, involving the development of models for automated analysis to achieve expert-level performance in routine clinical diagnostic tasks. Deep learning is also used to harness new knowledge by uncovering hidden patterns in data for better diagnosis, prognosis, and treatment responses.
Thus, this Special Issue will highlight the recent developments in and prospects of machine/deep learning methods and applications in cancer imaging. This includes the development and implementation of algorithms, data science, and new data management tools, the preclinical and clinical advancement of machine/deep learning, reviews, trends, and future aspects of machine/deep learning in cancer imaging.
Dr. Frezghi Habte
Guest Editor
Manuscript Submission Information
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Keywords
- deep learning
- machine learning
- medical imaging
- cancer
- imaging data science
- image analysis
- classification
- segmentation
- predication
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