Application of visual transformer in renal image analysis.
Biomed Eng Online
; 23(1): 27, 2024 Mar 05.
Article
en En
| MEDLINE
| ID: mdl-38439100
ABSTRACT
Deep Self-Attention Network (Transformer) is an encoder-decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with the inductive bias of convolutional neural network (CNN), Transformer has been gradually applied to medical image processing, including kidney image processing. It has become a hot research topic in recent years. To further explore new ideas and directions in the field of renal image processing, this paper outlines the characteristics of the Transformer network model and summarizes the application of the Transformer-based model in renal image segmentation, classification, detection, electronic medical records, and decision-making systems, and compared with CNN-based renal image processing algorithm, analyzing the advantages and disadvantages of this technique in renal image processing. In addition, this paper gives an outlook on the development trend of Transformer in renal image processing, which provides a valuable reference for a lot of renal image analysis.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Registros Electrónicos de Salud
Idioma:
En
Revista:
Biomed Eng Online
Asunto de la revista:
ENGENHARIA BIOMEDICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Reino Unido