bibtext: @InProceedings{ Qin_DBSAM_MICCAI2024,
                   author = { Qin, Chao and Cao, Jiale and Fu, Huazhu and Shahbaz Khan, Fahad and Anwer, Rao Muhammad },
                   title = { { DB-SAM: Delving into High Quality Universal Medical Image Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15012 },
                   month = {October},
                   pages = { pending },
                }
abstract: Recently, the Segment Anything Model (SAM) has demonstrated promising  segmentation capabilities in a variety of downstream segmentation tasks. However in the context of universal medical image segmentation there exists a notable performance discrepancy when directly applying SAM due to the domain gap between natural and 2D/3D medical data.  In this work, we propose a dual-branch adapted SAM framework, named DB-SAM, that strives to effectively bridge this domain gap. Our dual-branch adapted SAM contains two  branches in parallel: a ViT branch and a convolution branch. The ViT branch incorporates a learnable channel attention block after each frozen attention block, which captures domain-specific local features. On the other hand, the convolution branch employs a light-weight convolutional block to extract domain-specific shallow features from the input medical image. To perform cross-branch feature fusion, we design a bilateral cross-attention block and a ViT convolution fusion block, which dynamically combine diverse information of two branches for mask decoder. Extensive experiments on large-scale medical image dataset with various 3D and 2D medical segmentation tasks reveal the merits of our proposed contributions. On 21 3D medical image segmentation tasks, our proposed DB-SAM  achieves an absolute gain of 8.8\%, compared to a recent medical SAM adapter in the literature. Our code and models will be publicly released.
title: DB-SAM: Delving into High Quality Universal Medical Image Segmentation
authors: Qin, Chao Cao, Jiale Fu, Huazhu Shahbaz Khan, Fahad Anwer, Rao Muhammad