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

Ultrasound II: Endoscopic Ultrasound and FibroScan01:25

Ultrasound II: Endoscopic Ultrasound and FibroScan

489
Endoscopic Ultrasound (EUS) and FibroScan are valuable diagnostic tools in gastroenterology and hepatology, each with specific applications and techniques.
Endoscopic Ultrasound (EUS):
489

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 image of rare primary oral melanoma in a paediatric patient.

BMJ case reports·2025
Same author

Efficient Explainable Models for Alzheimer's Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning.

Diagnostics (Basel, Switzerland)·2025
Same author

Explainable and Interpretable Model for the Early Detection of Brain Stroke Using Optimized Boosting Algorithms.

Diagnostics (Basel, Switzerland)·2024
Same author

Unlocking Precision Medicine for Prognosis of Chronic Kidney Disease Using Machine Learning.

Diagnostics (Basel, Switzerland)·2023

相关实验视频

Updated: Jan 7, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

LivSCP:通过监督的对比预训练改善肝纤维化分类

Yogita Dubey1, Aditya Bhongade1, Punit Fulzele2

  • 1Department of Electronics & Telecommunication Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India.

Diagnostics (Basel, Switzerland)
|December 30, 2025
PubMed
概括

一种新的训练方法,LivSCP,使用超声波扫描来增强非侵入性肝纤维化分类. 它在不改变模型架构的情况下实现了最先进的结果,非常适合有限的数据场景.

关键词:
相反的学习学习学习.肝脏纤维化 肝脏纤维化预训练的预训练监督的对比学习学习.视觉变压器 视觉变压器

更多相关视频

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
03:38

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

Published on: June 20, 2025

810
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.8K

相关实验视频

Last Updated: Jan 7, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
03:38

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

Published on: June 20, 2025

810
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.8K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 肝病学 肝病学是一种肝病学.

背景情况:

  • 深度学习模型用于通过超声波进行非侵入性肝纤维化分类.
  • 尽管模型架构和培训方法的进步,性能改进却停滞不前.
  • 需要复杂的方法来提高分类准确性.

研究的目的:

  • 引入LivSCP,一种用于肝纤维化分类的新型培训方法.
  • 为了提高分类准确度,超越传统的监督学习 (SL).
  • 为具有有限标记数据和计算资源的设置提供解决方案.

主要方法:

  • 拟议的LivSCP培训方法用于肝纤维化分类.
  • 不需要对现有的网络架构或优化器进行修改.
  • 与SL和其他模型的基线视觉变压器进行评估.

主要成果:

  • 实现了最先进的性能,98.10%的准确性,精度,回忆和F1分数.
  • 在接收器运行特征曲线 (AUROC) 下的面积达到0.9972.
  • 在没有改变网络架构的情况下证明了有效性,适合低数据环境.

结论:

  • 成功开发了一种培训方法 (LivSCP),用于在低数据和计算环境下对肝纤维化进行分类.
  • LivSCP的性能超过了基线和多个模型,建立了最先进的性能.
  • 该方法在医疗图像分析中对资源有限的场景具有优势.