Application of deep learning in radiation therapy for cancer
Copyright © 2024 Société française de radiothérapie oncologique (SFRO). Published by Elsevier Masson SAS. All rights reserved..
In recent years, with the development of artificial intelligence, deep learning has been gradually applied to clinical treatment and research. It has also found its way into the applications in radiotherapy, a crucial method for cancer treatment. This study summarizes the commonly used and latest deep learning algorithms (including transformer, and diffusion models), introduces the workflow of different radiotherapy, and illustrates the application of different algorithms in different radiotherapy modules, as well as the defects and challenges of deep learning in the field of radiotherapy, so as to provide some help for the development of automatic radiotherapy for cancer.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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| Enthalten in: |
Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique - 28(2024), 2 vom: 18. Apr., Seite 208-217 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wen, X [VerfasserIn] |
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| Links: |
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| Themen: |
Apprentissage profond |
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| Anmerkungen: |
Date Completed 22.04.2024 Date Revised 22.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.canrad.2023.07.015 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2024 Société française de radiothérapie oncologique (SFRO). Published by Elsevier Masson SAS. All rights reserved. | ||
| 520 | |a In recent years, with the development of artificial intelligence, deep learning has been gradually applied to clinical treatment and research. It has also found its way into the applications in radiotherapy, a crucial method for cancer treatment. This study summarizes the commonly used and latest deep learning algorithms (including transformer, and diffusion models), introduces the workflow of different radiotherapy, and illustrates the application of different algorithms in different radiotherapy modules, as well as the defects and challenges of deep learning in the field of radiotherapy, so as to provide some help for the development of automatic radiotherapy for cancer | ||
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| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Dose | |
| 650 | 4 | |a Dose distribution | |
| 650 | 4 | |a Prognosis | |
| 650 | 4 | |a Pronostic | |
| 650 | 4 | |a Radiotherapy | |
| 650 | 4 | |a Radiothérapie | |
| 650 | 4 | |a Segmentation automatique | |
| 700 | 1 | |a Zhao, C |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, B |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, M |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, J |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, W |e verfasserin |4 aut | |
| 700 | 1 | |a Meng, J |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, L |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, S |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, J |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Y |e verfasserin |4 aut | |
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