Deep learning techniques for imaging diagnosis of renal cell carcinoma : current and emerging trends
Copyright © 2023 Wang, Zhang, Wang, Li, Zhang, Zhang, Xu, Jiao and Niu..
This study summarizes the latest achievements, challenges, and future research directions in deep learning technologies for the diagnosis of renal cell carcinoma (RCC). This is the first review of deep learning in RCC applications. This review aims to show that deep learning technologies hold great promise in the field of RCC diagnosis, and we look forward to more research results to meet us for the mutual benefit of renal cell carcinoma patients. Medical imaging plays an important role in the early detection of renal cell carcinoma (RCC), as well as in the monitoring and evaluation of RCC during treatment. The most commonly used technologies such as contrast enhanced computed tomography (CECT), ultrasound and magnetic resonance imaging (MRI) are now digitalized, allowing deep learning to be applied to them. Deep learning is one of the fastest growing fields in the direction of medical imaging, with rapidly emerging applications that have changed the traditional medical treatment paradigm. With the help of deep learning-based medical imaging tools, clinicians can diagnose and evaluate renal tumors more accurately and quickly. This paper describes the application of deep learning-based imaging techniques in RCC assessment and provides a comprehensive review.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:13 |
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| Enthalten in: |
Frontiers in oncology - 13(2023) vom: 28., Seite 1152622 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Zijie [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Revised 21.09.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fonc.2023.1152622 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM362240213 |
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| 520 | |a Copyright © 2023 Wang, Zhang, Wang, Li, Zhang, Zhang, Xu, Jiao and Niu. | ||
| 520 | |a This study summarizes the latest achievements, challenges, and future research directions in deep learning technologies for the diagnosis of renal cell carcinoma (RCC). This is the first review of deep learning in RCC applications. This review aims to show that deep learning technologies hold great promise in the field of RCC diagnosis, and we look forward to more research results to meet us for the mutual benefit of renal cell carcinoma patients. Medical imaging plays an important role in the early detection of renal cell carcinoma (RCC), as well as in the monitoring and evaluation of RCC during treatment. The most commonly used technologies such as contrast enhanced computed tomography (CECT), ultrasound and magnetic resonance imaging (MRI) are now digitalized, allowing deep learning to be applied to them. Deep learning is one of the fastest growing fields in the direction of medical imaging, with rapidly emerging applications that have changed the traditional medical treatment paradigm. With the help of deep learning-based medical imaging tools, clinicians can diagnose and evaluate renal tumors more accurately and quickly. This paper describes the application of deep learning-based imaging techniques in RCC assessment and provides a comprehensive review | ||
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| 700 | 1 | |a Wang, Xinning |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jianfei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Yuhao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Tianwei |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Shang |e verfasserin |4 aut | |
| 700 | 1 | |a Jiao, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Niu, Haitao |e verfasserin |4 aut | |
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