| Autor: |
Ruiling Xu, Jinxin Tang, Chenbei Li, Hua Wang, Lan Li, Yu He, Chao Tu, Zhihong Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Meta-Radiology, Vol 2, Iss 2, Pp 100069- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2950-1628 |
| DOI: |
10.1016/j.metrad.2024.100069 |
| Popis: |
Soft tissue sarcomas (STSs) represent a group of heterogeneous mesenchymal tumors of which are generally classified as per the histopathology. Despite being rare in incidence and prevalence, STSs are usually correlated with unfavorable prognosis and high mortality rate. Early and accurate diagnosis of STSs are critical in clinical management of STSs. Deep learning (DL) refers to a subtype of artificial intelligence that has been adopted to assist healthcare professionals to optimize personalized treatment for a given situation, particularly in image analysis. Recently, emerging studies have demonstrated that application of DL based on medical images could substantially improve the accuracy and efficiency of clinicians to the identification, diagnosis, treatment, and prognosis prediction of STSs, and thereby facilitating the clinical decision-making. Herein, we aimed to extensively summarize the recent applications of DL-based artificial intelligence in STSs from the aspects of data acquisition, algorithm, and model establishment. Besides, the reinforcement of the model by transfer learning and generative adversarial network (GAN) for data augmentation has also been elaborated. It is worth noting that high-quality data with accurate annotations, as well as optimized algorithmic performance are pivotal in the clinical application of DL in STSs. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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