SAM-Med2D系列工作开源链接:github.com/OpenGVLab/SA 数据集下载链接:openxlab.org.cn/dataset 背景 基础模型(Foundation Models)在自然语言、通用视觉等领域的巨大成功离不开海量的训练数据。以Segment Anything Model(SAM)为例,它通过在1100万图像和11亿掩膜(Mask)的数据上成功训练出了分割一切的基础分割模型。尽管...
(2023.12.05) We open the download of the dataset on the Hugging Face platform (2023.11.23) We have released the SA-Med2D-20M dataset (2023.11.21) We have released article introducing the SA-Med2D-20M dataset (2023.10.24) We now released SAM-Med3D, which focus on segmentation of 3D med...
modalities and objects. Then, we comprehensively fine-tune SAM on this dataset and turn it into SAM-Med2D. Unlike previous methods that only adopt bounding box or point prompts as interactive segmentation approach, we adapt SAM to medical image segmentation through more comprehensive prompts involving...
various modalities and objects. Then, we comprehensively fine-tune SAM on this dataset and turn it into SAM-Med2D. Unlike previous methods that only adopt bounding box or point prompts as Figure 1: Comparison between examples in SA-1B (a) and in our dataset (b). SA-1B consists of 11M...
SA-Med2D-20M，一项具有革命性的数据集项目，汇集了460万张医学图像与近2000万个对应的掩膜，涵盖了10种模态、31个主要器官及219个类别，成为全球最大的医学分割数据集。此数据集源于广泛公开与私人数据，旨在加速医学基础模型的研发，促进医学图像数据迭代，推动医疗应用领域向更通用方向发展。欢迎大家遵规...
SAM-Med2D represents an enhanced iteration of SAM, specifically tailored for medical image segmentation. This enhancement is accomplished through fine-tuning on the extensive medical image segmentation dataset, SA-Med2D-20M. As depicted in Fig.2, SAM-Med2D is designed with three components: an enc...
SAM-Med2D represents an enhanced iteration of SAM, specifically tailored for medical image segmentation. This enhancement is accomplished through fine-tuning on the extensive medical image segmentation dataset, SA-Med2D-20M. As depicted in Fig.2, SAM-Med2D is designed with three components: an enc...
4.1、Dataset ACDC(自动心脏诊断挑战)数据集是MICCAI 2017挑战的一部分,该挑战包含100名患者的心脏结构的MRI扫描,每个患者有2个3Dvolumes。该数据集还提供了左心室、右心室和心肌的专家分割Mask。 作者根据患者将MRI扫描随机分为三部分,训练集、验证集和测试集,比例为70:15:15。对于预处理,作者对每个volumes进行归一...
Dataset and evaluation metrics 3.1.1 Training and validation dataset 作者仅使用提供的挑战数据集,不使用其他公共数据集。该数据集包括11个模态:CT、MRI、PET、X射线、超声、乳腺摄影、OCT、内窥镜、眼底、皮肤镜和显微镜,总计超过100万个2D图像- Mask 对。
4.1、Dataset ACDC(自动心脏诊断挑战)数据集是MICCAI 2017挑战的一部分,该挑战包含100名患者的心脏结构的MRI扫描,每个患者有2个3Dvolumes。该数据集还提供了左心室、右心室和心肌的专家分割Mask。 作者根据患者将MRI扫描随机分为三部分,训练集、验证集和测试集,比例为70:15:15。对于预处理,作者对每个volumes进行归一...