Medical image analysis using improved SAM-Med2D: segmentation and classification perspectives.

Sun, Jiakang; Chen, Ke; He, Zhiyi; Ren, Siyuan; He, Xinyang; Liu, Xu; Peng, Cheng

Sun, Jiakang; Chen, Ke; He, Zhiyi; Ren, Siyuan; He, Xinyang; Liu, Xu; Peng, Cheng.

Afiliación

Sun J; Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, 610213, Sichuan, China.
Chen K; School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 101499, China.
He Z; Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, 610213, Sichuan, China.
Ren S; School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 101499, China.
He X; Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, 610213, Sichuan, China.
Liu X; School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 101499, China.
Peng C; Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, 610213, Sichuan, China.

BMC Med Imaging ; 24(1): 241, 2024 Sep 16.

Article en En | MEDLINE | ID: mdl-39285324

ABSTRACT

ABSTRACT

Recently emerged SAM-Med2D represents a state-of-the-art advancement in medical image segmentation. Through fine-tuning the Large Visual Model, Segment Anything Model (SAM), on extensive medical datasets, it has achieved impressive results in cross-modal medical image segmentation. However, its reliance on interactive prompts may restrict its applicability under specific conditions. To address this limitation, we introduce SAM-AutoMed, which achieves automatic segmentation of medical images by replacing the original prompt encoder with an improved MobileNet v3 backbone. The performance on multiple datasets surpasses both SAM and SAM-Med2D. Current enhancements on the Large Visual Model SAM lack applications in the field of medical image classification. Therefore, we introduce SAM-MedCls, which combines the encoder of SAM-Med2D with our designed attention modules to construct an end-to-end medical image classification model. It performs well on datasets of various modalities, even achieving state-of-the-art results, indicating its potential to become a universal model for medical image classification.

Asunto(s)

Algoritmos; Humanos; Procesamiento de Imagen Asistido por Computador/métodos; Interpretación de Imagen Asistida por Computador/métodos

Palabras clave

Attention mechanism; Classification and grading; Multimodal; SAM-Med2D; Segmentation

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos Límite: Humans Idioma: En Revista: BMC Med Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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