SAM-Med3D: 进一步加速数据和模型的生产与迭代 医学图像和自然图像之间存在显著的差异,而且医学图像领域缺乏大规模的基准数据集,这是导致AI在医学领域进展缓慢的重要原因之一。通过构建大规模的基准数据集和可靠的基线模型,我们可以推动AI在医疗领域的快速发展,并加速医疗向更通用方向转变的进程。如果您对此话题感兴趣,欢...
Baseline 为 SAM-Med3D 微调前的性能,可以看到在新的下游任务上表现不够好,因为训练数据中没有对应分布的图像;FT-expert 为仅仅在 SPPIN2023 上微调 Mask Decoder的性能表现,可以看出虽然在 SPPIN 上效果较为优秀,但是在之前的任务上性能降低很多;作者提出的 SAM-Med3D-MoE 可以看出,在之前的任务上性能几乎没有...
SAMMed ,这是一个利用SAM功能的医学图像标注增强框架。 SAMMed 框架由2个子模块组成,即SAMassist 和SAMauto : SAMassist 使用即时学习方法展示了SAM对下游医学分割任务的泛化能力。结果显示,仅使用大约5个输入点就显著提高了分割精度。 SAMauto 模型旨在通过自动生成输入Prompt来加快标注过程。 所提出的SAP-Net模型...
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...
The meaning of SAMMED is past tense of sam.
🏆 The most comprehensive fine-tuning based on Segment Anything Model (SAM). 🏆 Comprehensive evaluation of SAM-Med2D on large-scale datasets. 🔥 Updates (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 datase...
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...
To address this, we propose SAM-Med3D-MoE, a novel framework that seamlessly integrates task-specific finetuned models with the foundational model, creating a unified model at minimal additional training expense for anextra gating network. This gating network, in conjunction with a selection ...
研究medsam参数量需结合实际应用。参数量增加可能提升medsam预测精度。确定medsam参数量要权衡资源与效果。Medsam参数量反映模型的复杂程度。小参数量medsam可能存在欠拟合风险。行业内对medsam参数量有多种观点。优化medsam参数量可提升整体效能。参数量影响medsam在复杂任务的表现。选择medsam参数量要参考历史经验。Med...
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 medical image segmentation. However, its reli...