图1:SAM 相关的模型在三维医学图像数据上的表现,SAM 和 SAM-Med2D 在空间上都出现了断层的现象,而 SAM-Med3D 在空间上具有更好的连贯性。 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...
比如,MedSAM 是一种典型示例,它通过使用110万个掩码(mask)对SAM 的解码器(Mask Decoder)进行微调,从而使 SAM 能够通过边界框(Bounding Box)作为提示来更好地分割医学影像;SAM-Med2D 则引入了适配器(Adapter)和约2000万个掩码(mask)对 SAM 进行了充分微调,从而在医学图像分割中表现出了卓越的性能。 然而,这些方...
在SAM原有的三个组件框架下,SAM-Med2D模型采用adapter机制,我们冻结图像编码器,并在每个Transformer块中合并可学习的适配器层,以获取医疗领域的特定领域知识,通过点、Bbox和掩码信息对提示编码器Prompt Encoder进行微调,同时通过交互训练更新掩码解码器Mask Encoder的参数。 Adapter 机制是一种在深度学习中用于模型可迁移...
Adapting Segment Anything Model for Medical Image Segmentation - Medical-SAM-Adapter/precpt.py at main · SuperMedIntel/Medical-SAM-Adapter
● Medical SAM Adapter 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 Di...
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。
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 ...
如图4所示,SAM-Med2D由三个主要组件组成:image encoder、prompt encoder和mask decoder。该框架允许在不同提示下为同一图像生成不同的mask。对于image encoder,在微调时冻结了原始图像编码器中的所有参数,并为每个Transformer块部署了一个...
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 ...
MedSAM's accuracy. We introduce MedSAM-U, an uncertainty-guided framework designed to automatically refine multi-prompt inputs for more reliable and precise medical image segmentation. Specifically, we first train a Multi-Prompt Adapter integrated with MedSAM, creating MPA-MedSAM, to adapt to ...