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Ladder Fine-tuning approach for SAM integrating complementary network

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arxiv 2306.12737 v1 pith:MGBSO4DE submitted 2023-06-22 cs.CV

Shurong Chai , Rahul Kumar Jain , Shiyu Teng , Jiaqing Liu , Yinhao Li , Tomoko Tateyama , Yen-wei Chen This is my paper · ORCID
classification cs.CV
keywords modelsmedicalnetworkgeneralizedavailablecomplementaryeffectivefine
verification ladder T0 review T1 audit T2 compute T3 formal
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Recently, foundation models have been introduced demonstrating various tasks in the field of computer vision. These models such as Segment Anything Model (SAM) are generalized models trained using huge datasets. Currently, ongoing research focuses on exploring the effective utilization of these generalized models for specific domains, such as medical imaging. However, in medical imaging, the lack of training samples due to privacy concerns and other factors presents a major challenge for applying these generalized models to medical image segmentation task. To address this issue, the effective fine tuning of these models is crucial to ensure their optimal utilization. In this study, we propose to combine a complementary Convolutional Neural Network (CNN) along with the standard SAM network for medical image segmentation. To reduce the burden of fine tuning large foundation model and implement cost-efficient trainnig scheme, we focus only on fine-tuning the additional CNN network and SAM decoder part. This strategy significantly reduces trainnig time and achieves competitive results on publicly available dataset. The code is available at https://github.com/11yxk/SAM-LST.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation

    cs.CV 2025-01 conditional novelty 5.0 of 10

    Prototype-guided prompt learning lets a SAM variant reach 78.75% mean Dice on Synapse and 76.39% on a ventricle dataset using 10% of training slices, beating SAMed and other prompt-free SAM baselines.

  2. EchoONE: Segmenting Multiple echocardiography Planes in One Model

    cs.CV 2024-12 conditional novelty 5.0 of 10

    EchoONE, a SAM-based model with prototype-composed dense prompts and a local-feature fusion branch, segments multiple echocardiography planes in one model and reports state-of-the-art Dice scores on internal and exter...

  3. Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine

    cs.CV 2024-11 conditional novelty 5.0 of 10

    An image-feature scoring engine plus k-means clustering selects prompt frames, improving SAM2-based segmentation in seven medical modalities.

  4. Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction

    eess.IV 2024-11 conditional novelty 5.0 of 10

    OVS-Net, a SAM-based vessel segmentation framework with a micro-vessel enhancement branch and morphology-correction post-processing, reports higher Dice and better connectivity than six SAM baselines and 17 expert mod...

  5. Parameter-Efficient Fine-Tuning for Foundation Models

    cs.CL 2025-01 conditional novelty 2.0 of 10

    A survey that categorizes and summarizes parameter-efficient fine-tuning methods across large language, vision, and multimodal models.

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