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Medical image analysis using improved SAM-Med2D: segmentation and classification perspectives.
- Source :
- BMC Medical Imaging; 9/16/2024, Vol. 24 Issue 1, p1-17, 17p
- Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14712342
- Volume :
- 24
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- BMC Medical Imaging
- Publication Type :
- Academic Journal
- Accession number :
- 179667855
- Full Text :
- https://doi.org/10.1186/s12880-024-01401-6