Ladder Fine-tuning approach for SAM integrating complementary network

• URL: http://arxiv.org/abs/2306.12737v1
• Date: Thu, 22 Jun 2023 08:36:17 GMT
• Title: Ladder Fine-tuning approach for SAM integrating complementary network
• Authors: Shurong Chai, Rahul Kumar Jain, Shiyu Teng, Jiaqing Liu, Yinhao Li, Tomoko Tateyama, Yen-wei Chen
• Abstract summary: 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. In this study, we propose to combine a complementary Convolutional Neural Network (CNN) along with the standard SAM network for medical image segmentation. This strategy significantly reduces trainnig time and achieves competitive results on publicly available dataset.
• Score: 5.46706034286531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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