{"ID":2844256,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08626","arxiv_id":"2511.08626","title":"SAMora: Enhancing SAM through Hierarchical Self-Supervised Pre-Training for Medical Images","abstract":"The Segment Anything Model (SAM) has demonstrated significant potential in medical image segmentation. Yet, its performance is limited when only a small amount of labeled data is available, while there is abundant valuable yet often overlooked hierarchical information in medical data. To address this limitation, we draw inspiration from self-supervised learning and propose SAMora, an innovative framework that captures hierarchical medical knowledge by applying complementary self-supervised learning objectives at the image, patch, and pixel levels. To fully exploit the complementarity of hierarchical knowledge within LoRAs, we introduce HL-Attn, a hierarchical fusion module that integrates multi-scale features while maintaining their distinct characteristics. SAMora is compatible with various SAM variants, including SAM2, SAMed, and H-SAM. Experimental results on the Synapse, LA, and PROMISE12 datasets demonstrate that SAMora outperforms existing SAM variants. It achieves state-of-the-art performance in both few-shot and fully supervised settings while reducing fine-tuning epochs by 90%. The code is available at https://github.com/ShChen233/SAMora.","short_abstract":"The Segment Anything Model (SAM) has demonstrated significant potential in medical image segmentation. Yet, its performance is limited when only a small amount of labeled data is available, while there is abundant valuable yet often overlooked hierarchical information in medical data. To address this limitation, we dra...","url_abs":"https://arxiv.org/abs/2511.08626","url_pdf":"https://arxiv.org/pdf/2511.08626v1","authors":"[\"Shuhang Chen\",\"Hangjie Yuan\",\"Pengwei Liu\",\"Hanxue Gu\",\"Tao Feng\",\"Dong Ni\"]","published":"2025-11-09T03:54:51Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.AI\",\"cs.CV\"]","methods":"[\"LoRA\"]","has_code":false,"code_links":[{"ID":607275,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2844256,"paper_url":"https://arxiv.org/abs/2511.08626","paper_title":"SAMora: Enhancing SAM through Hierarchical Self-Supervised Pre-Training for Medical Images","repo_url":"https://github.com/ShChen233/SAMora","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}