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Machine Learning in Radiomic Renal Mass Characterization: Fundamentals, Applications, Challenges, and Future
Burak Kocak1, Ece Ates Kus1, Aytul Hande Yardimci1
1Department of Radiology, Istanbul Training and Research Hospital, Samatya, Istanbul 34098, Turkey.
Abstract:
OBJECTIVE. The purpose of this study is to provide an overview of the traditional machine learning (ML)-based and deep learning-based radiomic approaches, with focus placed on renal mass characterization. CONCLUSION. ML currently has a very low barrier to entry into general medical practice because of the availability of many open-source, free, and easy-to-use toolboxes. Therefore, it should not be surprising to see its related applications in renal mass characterization. A wider picture of the previous works might be beneficial to move this field forward.
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