Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

Self-contrastive weakly supervised learning framework for prognostic prediction using whole slide images.

PLOS digital health·2025

Same author

Deep Learning Models Connecting Images and Text: A Primer for Radiologists.

Radiographics : a review publication of the Radiological Society of North America, Inc·2025

Same author

Towards reliable WMH segmentation under domain shift: An application study using maximum entropy regularization to improve uncertainty estimation.

Computers in biology and medicine·2025

Same author

Neighbor-aware calibration of segmentation networks with penalty-based constraints.

Medical image analysis·2025

Same author

Harmonizing flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization.

Medical image analysis·2025

Same author

A Foundation Language-Image Model of the Retina (FLAIR): encoding expert knowledge in text supervision.

Medical image analysis·2024

Search research articles

相关实验视频

Updated: May 13, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

走向基础模型和对体积器官细分的几个镜头参数高效微调.

Julio Silva-Rodríguez¹, Jose Dolz², Ismail Ben Ayed²

¹ÉTS Montréal, Québec, Canada.

Medical image analysis

|May 10, 2025

概括

基础模型对医疗图像细分有希望,但需要有效的适应. 采用参数效率的方法和适配器的短拍效率微调 (FSEFT) 为稀缺数据场景提供了资源意识的解决方案.

科学领域:

人工智能的人工智能
医疗成像医学成像
计算机视觉计算机视觉

背景情况:

基础模型和预训练和适应范式对于体积医学图像细分越来越受欢迎.
转移学习的完整微调策略可能是资源密集型的,并且在有限的标记数据下表现不佳,阻碍了临床应用.

研究的目的:

为了正式制定Few-Shot高效微调 (FSEFT) 作为适应医疗图像细分基础模型的现实场景.
为了应对数据和参数效率在调整基础模型医疗图像细分的挑战.

主要方法:

利用在CT器官细分数据上预先训练的基础模型.
使用了参数高效微调和黑盒适配器,以实现高效的适应.
引入了新的空间黑子适配器,用于密集的预测任务和传导推理,结合特定任务的先前知识.

主要成果:

证明了基础模型对于医学图像细分的适用性.
突出了一些热门的微调策略在短暂的场景中的局限性.
展示了提出的高效适应方法的有效性.

结论:

基础模型对于医疗图像细分是有效的,但高效的适应技术对于具有有限数据和资源的现实临床环境至关重要.

关键词:

黑盒子适配器黑盒子适配器有几次拍摄的适应适应.基金会模型基金会模型对参数进行高效微调.量度细分是指体积的细分.

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

相关实验视频

Last Updated: May 13, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

使用参数高效微调和新型适配器的FSEFT提供了一个可行的方法,可以在数据限制下适应这些模型.