ArticleBMC medical imaging2024
Medical image analysis using improved SAM-Med2D: segmentation and classification perspectives.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- Decipher-MR: a vision-language foundation model for 3D MRI representations.NPJ digital medicine · 2026Article
- StructSAM: structure-aware prompt adaptation for robust lung cancer lesion segmentation in CT.NPJ digital medicine · 2026Article
- Prompt-mamba filtering networks for accurate hepatocellular carcinoma lesion segmentation in abdominal CT.NPJ digital medicine · 2026Article
- Endo-MedSAM: a promptable vision foundation model adaptation for uterus segmentation on pelvic MRI in endometriosis.Frontiers in reproductive health · 2026Article
- A Review of Deep Learning Approaches Based on Segment Anything Model for Medical Image Segmentation.Bioengineering (Basel, Switzerland) · 2025Review
- Efficient 3D Biomedical Image Segmentation by Parallelly Multiscale Transformer-CNN Aggregation Network.Chemical & biomedical imaging · 2025Article
- Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000.Frontiers in neurology · 2025Review
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Authors and funding
7 authors.
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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.
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