评估SAM-Med2D 实验结果 定量评估 定性评估 总结 由于医学图像和自然图像之间存在较大差异,以及缺少大规模医学图像基准数据集,这是导致AI在医学领域进展缓慢的原因之一。构建大规模基准数据集和可靠的基线模型,能够推动AI在医疗领域的快速发展,加速医疗向更通用的方向转变。欢迎感兴趣的读者加入群聊与我们讨论!(二维码见...
为了推进基础模型在医学图像分析中的发展,来自上海人工智能实验室通用视觉团队的研究者们通过收集和整理大量的公开和私人数据集后构建了一个超大规模的医学图像分割数据集: SA-Med2D-20M(图2),该数据集共有460万张医学图像和1970万个相应的掩膜,涵盖了10种模态、31个主要器官和219个类别,其分割目标覆盖了几乎全身,...
The pipeline of SAM-Med2D. We freeze the image encoder and incorporate learnable adapter layers in each Transformer block to acquire domain-specific knowledge in the medical field. We fine-tune the prompt encoder using point, Bbox, and mask information, while updating the parameters of the mask...
The pipeline of SAM-Med2D. We freeze the image encoder and incorporate learnable adapter layers in each Transformer block to acquire domain-specific knowledge in the medical field. We fine-tune the prompt encoder using point, Bbox, and mask information, while updating the parameters of the mask...
Recently emerged SAM-Med2D represents a state-of-the-art advancement in medical image segmentation. Through fine-tuning the Large Visual Model, Segment Anything Model (SAM), on extensive medical datasets, it has achieved impressive results in cross-modal
类别信息统计 SA-Med2D-20M包含219个类别标签，类别分布为长尾类型。最常见的类别掩膜数量在10,000至100,000之间，最常见的是增强性肿瘤和水肿。联合类别用于处理多个类别之间的像素重叠问题，标签未知类别指原始数据集未提供特定标签信息。构建流程 数据集构建涉及数据收集、图像归一化和掩膜处理。图像...
Recently emerged SAM-Med2D represents a state-of-the-art advancement in medical image segmentation. Through fine-tuning the Large Visual Model, Segment Anything Model (SAM), on extensive medical datasets, it has achieved impressive results in cross-modal
Du kan behöva logga in på ditt konto. Mer information finns iSamarbeta i Excel-arbetsböcker samtidigt med andra med samtidig redigering. Dela din arbetsbok I det övre högra hörnet i arbetsboken väljer duDelaoch sedanDelapå menyn. ...
Du skal muligvis logge på din konto. Hvis du vil have mere at vide, skal du læseSamarbejd om Excel-projektmapper på samme tid med samtidig redigering. Del din projektmappe VælgDeli øverste højre hjørne af projektmappen, og vælg derefterDeli menue...
Title题目SAM-Med2D01文献速递介绍医学图像分割在通过识别和勾画各种组织、器官或感兴趣区域来分析医学图像中发挥着至关重要的作用。准确的分割可以帮助医生精确识别和定位病理区域,从而实现更准确的诊断和治疗。此外,对医学图像进行定量和定性分析能够提供对不同组织或器官的形态、结构和功能的全面洞察,促进疾病研究和发现...