Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
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 JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

102
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
102

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

Disparate privacy risks from medical AI.

Nature·2026
Same author

Delphi consensus on ex vivo fluorescence confocal microscopy in robot-assisted radical prostatectomy.

BJU international·2026
Same author

Deep learning for fluorescence confocal microscopy image interpretation in radical prostatectomy.

BJU international·2026
Same author

Latent Causal Modeling for 3D Brain MRI Counterfactuals.

Deep generative models : 5th MICCAI workshop, DGM4MICCAI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings. DGM4MICCAI (Workshop) (5th : 2025 : Taejon-si, Korea)·2026
Same author

Average Calibration Losses for Reliable Uncertainty in Medical Image Segmentation.

IEEE transactions on medical imaging·2026
Same author

End-to-end integrative segmentation and radiomics prognostic models for risk stratification of high-grade serous ovarian cancer: a retrospective multicohort study.

The Lancet. Digital health·2026
Same journal

Correspondence-free local-to-global liver deformation correction via implicit neural representation and biomechanical model.

International journal of computer assisted radiology and surgery·2026
Same journal

BronchoLumen: analysis of recent YOLO-based architectures for real-time bronchial orifice detection in video bronchoscopy.

International journal of computer assisted radiology and surgery·2026
Same journal

Model-guided medicine for early diagnosis of transthyretin-associated cardiac amyloidosis using multimodal data integration and standardized interoperable models (the CRONOS-ATTR study).

International journal of computer assisted radiology and surgery·2026
Same journal

Electromagnetic navigation for femoral osteotomy using high-accuracy X-ray-to-CT registration.

International journal of computer assisted radiology and surgery·2026
Same journal

Modular instrument actuation unit for robotic-assisted systems in laparoscopic surgery.

International journal of computer assisted radiology and surgery·2026
Same journal

Pose-aware deep perceptual similarity for iterative 2D/3D registration of knee joints using contrastive learning.

International journal of computer assisted radiology and surgery·2026
查看所有相关文章

相关实验视频

Updated: Jun 26, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K

使用具有离散表示的变压器进行稳健的前列腺疾病分类.

Ainkaran Santhirasekaram1, Mathias Winkler2, Andrea Rockall2

  • 1Department of Computing, Imperial College London, London, UK. a.santhirasekaram19@imperial.ac.uk.

International journal of computer assisted radiology and surgery
|May 13, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的框架,使用离散表示来增强前列腺MRI疾病分类视觉变压器模型的稳定性,改善跨不同磁场强度的概括性.

关键词:
生物医学成像学 生物医学成像学计算机辅助诊断是一种计算机辅助的诊断.机器学习 机器学习神经网络的神经网络的神经网络坚固性 坚固性

更多相关视频

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

169
Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
09:11

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

21.5K

相关实验视频

Last Updated: Jun 26, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

169
Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
09:11

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

21.5K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 卷积神经网络 (CNN) 在使用多参数MRI的前列腺疾病自动分类方面表现有前途.
  • 视觉变压器 (ViTs),一种没有CNN的架构,在一些图像分类任务中表现出色,但由于不同的采集协议,在MRI中常见的纹理变化难以应对.
  • 将MRI模型推广到不同的磁铁强度 (例如1.5T与3T) 仍然是一个挑战.

研究的目的:

  • 开发一个更强大的视力转换器 (ViT) 框架用于MRI前列腺癌分类.
  • 提高ViT模型对不同磁场强度和采集协议的概括能力.
  • 解决ViTs在MRI数据中对纹理转移的脆弱性.

主要方法:

  • 提出了一个新的框架,通过矢量量化使用离散数据表示来增强ViT的稳定性.
  • 变压器模型的输入由这些离散表示的子集组成.
  • 利用交叉注意力来整合T2加权和明显扩散系数 (ADC) MRI图像的离散表示.

主要成果:

  • 拟议的方法在分类前列腺MRI病变方面展示了最先进的 (SOTA) 性能.
  • 与现有的CNN和变压器模型相比,实现了对域移动 (例如1.5T与3T扫描仪) 的优越稳定性.
  • 展示了对输入空间扰动的提高弹性.

结论:

  • 开发了一种有效的方法,以提高MRI上的基于变压器的前列腺病变分类的稳定性.
  • 使用T2加权和ADC图像的离散表示,可以显著提高不同MRI扫描仪强度的模型概括性.
  • 该框架为在MRI数据变化的情况下可靠的自动疾病分类提供了一个有希望的解决方案.