除了将 SAM 直接应用于三维数据,一些研究人员希望通过引入二维到三维的适配器(Adapter)来捕捉三维空间信息。如图2所示,这些方法通常在保持编码器(Image Encoder)不变的同时引入了三维适配器(Adapter),以使模型能够从三维图像中学习到三维空间信息。然而,这些方法存在两个主要限制: 数据规模有限:这些方法的模型通常只在有...
Adapter Layer 中的"Up", "ReLU", "Down" 层是指 Adapter 的三个部分,它们组成了Adapter的核心结构。1.Up Layer(上层): 这一部分负责将输入的特征映射到一个更高维度的空间。它可以是线性映射或者其他类型的层,用于将输入 特征进行转换。 功能:将输入特征映射到一个更高维度的表示空间,以便进行更多的特征学习...
Medical SAM Adapter, or say MSA, is a project to fineturn SAM using Adaption for the Medical Imaging. This method is elaborated on the paper Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation. A Quick Overview News [TOP] Join in our Discord to ask questions ...
adapter模型数据性能优化 作为在超过1100万张图像上预训练的大型视觉模型,Segment-Anything Model (SAM)[1]引起了研究行人的关注。然而,最近的研究表明,SAM在下游任务上表现不佳,包括伪装物检测[2,3]、阴影检测[3]和显著物检测[4]。 未来先知 2024/08/20 3960 将Segment Anything扩展到医学图像领域 编码工具模型...
For training the adapter, run:python train.py -net sam -mod sam_adpt -exp_name REFUGE-MSAdapt -sam_ckpt ./checkpoint/sam/sam_vit_b_01ec64.pth -image_size 1024 -b 32 -dataset REFUGE -data_path ./data/REFUGE-MultiRateryou can change "exp_name" to anything you want. ...
类似于SAM,SAM2在使用点提示和箱形提示的医学图像上表现不佳。SAM2发布后,一些基于SAM在医学图像领域的成功适应性的研究,旨在将SAM2应用于医学图像领域。例如,MedicalSAM2 [19]和MedSAM [10]微调口罩解码器,而SAM2-Adapter [1]将轻量级 Adapter 引入图像编码器,并在权重更新期间与口罩解码器一起进行微调。
括号中的数字表示5点提示后Dice分数的增量,红色表示提高,绿色表示下降。 Table 4: Generalization validation on 9 MICCAI2023 datasets, where "*" denotes SAM-Med2D without adapter layer parameters.表4:在9个MICCAI2023数据集上的泛化验证,其中“*”表示没有适配器层参数的SAM-Med2D。
Since medical image segmentation needs to predict detailed segmentation boundaries, we designed a novel adapter to enhance the SAM features with high-frequency information during Parameter-Efficient Fine-Tuning (PEFT). To convert the SAM features and coordinates into continuous segmentation output, we ...
Specifically, we first train a Multi-Prompt Adapter integrated with MedSAM, creating MPA-MedSAM, to adapt to diverse multi-prompt inputs. We then employ uncertainty-guided multi-prompt to effectively estimate the uncertainties associated with the prompts and their initial segmentation results. In ...
Specifically, we first train a Multi-Prompt Adapter integrated with MedSAM, creating MPA-MedSAM, to adapt to diverse multi-prompt inputs. We then employ uncertainty-guided multi-prompt to effectively estimate the uncertainties associated with the prompts and their initial segmentation results. In ...