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

Long-patch Base Excision Repair01:02

Long-patch Base Excision Repair

7.2K
Since the discovery of the two BER pathways, there has been a debate about how a cell chooses one pathway over the other and the factors determining this selection. Numerous in vitro experiments have pointed out multiple determinants for the sub-pathway selection. These are:
7.2K
Block Diagram Reduction01:22

Block Diagram Reduction

288
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
288

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

Clinical artificial intelligence applications of vision-language foundation models.

PLOS digital health·2026
Same author

A Knowledge-Guided Bi-Modal Network for the Classification of Anterior Chamber Angle Images.

IEEE journal of biomedical and health informatics·2026
Same author

Imaging of Guest Molecule Adsorption onto 2D Covalent Organic Frameworks by Scanning Tunneling Microscopy.

ACS nano·2025
Same author

Multi-sequence brain tumor segmentation boosted by deep semantic features.

Medical physics·2025
Same author

Structure-Aware Brain Tissue Segmentation for Isointense Infant MRI Data Using Multi-Phase Multi-Scale Assistance Network.

IEEE journal of biomedical and health informatics·2024
Same author

The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image Prior.

Proceedings. IEEE International Conference on Computer Vision·2024
Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026
Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026
Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026
Same journal

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026
Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026
Same journal

Annotation-efficient medical image segmentation via cross-latent graphs and vector-quantized memory.

Medical image analysis·2026
查看所有相关文章

相关实验视频

Updated: Sep 14, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K

通过序列编码和块平衡进行ROP损伤细分.

Xiping Jia1, Jianying Qiu2, Dong Nie3

  • 1School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China.

Medical image analysis
|July 24, 2025
PubMed
概括
此摘要是机器生成的。

一个新的AI模型,SeBSNet,准确地检测出早产视网膜病变 (ROP) 的微妙病变,这是婴儿失明的主要原因. 这种先进的细分网络可以提高ROP的诊断准确性,帮助及时的临床治疗.

关键词:
区块加权的平衡方式域名知识编码 域名知识编码过早生育的视网膜病变细分网络的细分网络是一个细分网络.序列编码学习学习

更多相关视频

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.3K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.4K

相关实验视频

Last Updated: Sep 14, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.3K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.4K

科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 早产视网膜病变 (ROP) 是早产婴儿视力丧失的重要原因之一.
  • 准确检测和细分ROP病变对于诊断和治疗至关重要.
  • 微妙和小的ROP病变给人类专家和自动化系统带来了诊断挑战.

研究的目的:

  • 开发和评估一种新的深度学习模型,以改善早产病变视网膜病变的细分.
  • 解决自动诊断系统中微妙和小的ROP病变所带来的挑战.

主要方法:

  • 引入序列编码和基于区块平衡的细分网络 (SeBSNet).
  • 集成领域知识编码,序列编码学习 (SCL) 和块加权平衡 (BWB) 技术.
  • 使用SeBSNet对早产病变视网膜病变的细分.

主要成果:

  • 与最先进的方法相比,SeBSNet在ROP损伤细分方面取得了更好的表现.
  • 获得的平均ROC_AUC为98.84%,PR_AUC为71.90%,子得分为66.88%.
  • 将SeBSNet技术纳入ROP分类网络显著提高了分类性能.

结论:

  • SeBSNet提供了一个强大而有效的解决方案,用于ROP病变的自动细分.
  • 拟议的模型有望改善早产儿视网膜病变的诊断和管理.
  • 开发的技术可以在ROP分析中增强细分和分类任务.