… to the original tasks that the Baseline SAM-Med3D had previously learned), resulting in
its performance being lower than the baseline. Our SAM-Med3D-MoE address this issue by …

… We comprehensively evaluate SAM-Med3D on 16 datasets covering diverse medical … The
evaluation demonstrates the efficiency and efficacy of SAM-Med3D, as well as its promising …

… Building upon SAM-Med3D, we propose an enhanced segmentation pipeline specifically
designed for the coreset track that incorporates advanced postprocessing techniques to …

… We introduce FastSAM3D, a computationally efficient adaptation of SAM-Med3D [27], also
… , in contrast to 12 heads per block in SAM-Med3D’s ViT architecture. Moreover, we retain the …

… Evaluation was performed using a sliding window, with input prompts generated by the
original SAM-Med3D based on 11 random ROI clicks. For evaluation, the Intersection over Union …

… Although the segmentation results of DSAM and SAMMed3D appear similar, Table II highlights
the … Specifically, compared to the traditional SAMMed3D, DSAM achieves a significant …

… Qiao, “Sam-med3d: Towards general-purpose segmentation models for volumetric
medical images,” arXiv preprint arXiv:2310.15161, 2024. [22] C. Chen, J. …

… Benefiting from the release of the large-scale medical image segmentation model SAM-Med3D,
which leverages a massive medical imaging dataset for pre-training, we introduce an …

… , this paper proposes a registration optimization method based on SAM-Med3D and
dynamic large kernel convolution. A fixed SAM-Med3D encoder was used to extract multi-scale …

… SAM-Med3D We design disparity blocks and LoRA to customize the attention mechanism of
SAM-Med3D, … As shown in Fig.1, the Fine-Tuned SAM-Med3D encoder consists of a Patch …