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

A lightweight network for segmenting tree-like structures in medical images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same author

Reconstruction of the Achilles tendon using LARS artificial ligament in bilateral xanthoma: a case report and literature review.

BMC surgery·2026

Same author

Affinity-Tuned Albumin Hitchhiking Extends the Bioorthogonal Capture Window in Pretargeting Radiotheranostics.

Advanced healthcare materials·2026

Same author

The effect of active warming and conventional body temperature management on perioperative blood loss and coagulation function in patients undergoing scoliosis correction: a multicenter randomized controlled trial.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2026

Same author

Multimodal animal health monitoring in extensive livestock production systems.

Frontiers in veterinary science·2026

Same author

Cell segmentation in microscopy images using a SAM-based U-Net architecture and a novel dataset.

Computer methods and programs in biomedicine·2026

Search research articles

相关实验视频

Updated: Sep 11, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

EPDiff:在手术前多模式图像中进行无监督异常检测的擦除感知扩散模型.

Jiazheng Wang, Min Liu, Wenting Shen

IEEE transactions on medical imaging

|August 11, 2025

概括

本研究介绍了 Erasure Perception Diffusion (EPDiff) 模型,用于在多模式图像中无监督检测异常. EPDiff有效地重建了亚健康结构,并提高了异常检测的准确性,优于现有的方法.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

无监督异常检测 (UAD) 与模仿异常的不健康区域作斗争.
多模式图像提供补充信息,以改善UAD.
现有的UAD方法往往无法准确地重建异常附近的复杂结构.

研究的目的:

为手术前多式影像提出一种新的无监督异常检测方法.
为了增强在异常周围的亚健康结构的重建.
用多式联运数据提高异常检测的准确性和稳定性.

主要方法:

清除感知扩散模型 (EPDiff) 包含本地清除渐进培训 (LEPT) 和全球结构感知 (GSP) 模块.
通过逐步删除和重建异常,LEPT使用两阶段的过程来学习亚健康结构.
GSP模块捕捉到图像模式内和图像模式之间的全球结构相关性.
多模式注意力融合 (MAF) 模块用于无训练的异常图的融合.

主要成果:

在BraTS2021和Shifts数据集上,EPDiff表现出卓越的性能.
在BraTS2021上实现了AUPRC的2%改善和mDice的3.9%.

更多相关视频

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

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

相关实验视频

Last Updated: Sep 11, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

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

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

在AUPRC实现了5.2%的改进,在mDice on Shifts实现了4.5%.

在无监督的异常检测任务中超越了最先进的方法.

结论:

通过利用多式联运数据,EPDiff有效地解决了传统UAD方法的局限性.
拟议的方法在检测和重建医学图像中的异常方面取得了显著的改进.
在各种异常诊断场景中,EPDiff具有广泛的适用性.