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

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

Electrochemical Probing of Dopamine Dynamics During Poly(I:C)-Induced Neuroinflammation.

Small (Weinheim an der Bergstrasse, Germany)·2024
Same author

Stratified analysis identifies HIF-2<i>α</i> as a therapeutic target for highly immune-infiltrated melanomas.

bioRxiv : the preprint server for biology·2024
Same author

Machine learning-based analysis identifies a 13-gene prognostic signature to improve the clinical outcomes of colorectal cancer.

Journal of gastrointestinal oncology·2024
Same author

HSTrans: Homogeneous substructures transformer for predicting frequencies of drug-side effects.

Neural networks : the official journal of the International Neural Network Society·2024
Same author

Physical Origin of the Ferroelectric-Type Hysteresis in MIM Structures with Amorphous Dielectric Film.

ACS applied materials & interfaces·2024
Same author

CD36-mediated arachidonic acid influx from decidual stromal cells increases inflammatory macrophages in miscarriage.

Cell reports·2024

相关实验视频

Updated: May 15, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K

通过改进的点教师来加强多体查,点教师弱半监督.

Xiuquan Du1, Xuejun Zhang2, Jiajia Chen2

  • 1Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China.

Computers in biology and medicine
|April 8, 2025
PubMed
概括

这项研究引入了一种用于检测结肠的新方法,改善了早期结肠直肠癌检测. 这种方法通过结合CNN和变压器功能来提高细分性能,即使数据有限.

关键词:
提取边界的提取方法特性蒸的特点是医疗图像细分 医疗图像细分软弱的半监督 半监督 软弱的半监督

更多相关视频

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.5K

相关实验视频

Last Updated: May 15, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.5K

科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 在瘤学瘤学.

背景情况:

  • 结肠多是结肠直肠癌的前体,需要早期检测.
  • 目前的选方法面临的挑战是由于有限的注释数据和图像质量问题,如模糊的边界和低对比度.
  • 现有的弱半监督方法往往忽视了教师模型中有价值的中间特征.

研究的目的:

  • 为具有有限注释数据的场景开发有效的多细分方法.
  • 为应对因数据稀缺和图像质量导致的聚检测性能差的挑战.
  • 改进医疗图像分析现有的弱半监督学习技术.

主要方法:

  • 利用卷积神经网络 (CNN) 和变压器的综合优势,实现强大的多特征提取 (边界和上下文).
  • 实施一种新的教师-学生中间特征蒸方法,以指导学生的模型学习.
  • 利用适应复杂医疗图像和有限注释的点提示教师模型.

主要成果:

  • 拟议的方法证明了有效处理具有有限注释的场景.
  • 在结肠多瘤方面取得了良好的细分性能.
  • 整合CNN和变压器的诱导偏差证明对特征表示有好处.

结论:

  • 开发的方法提供了一个有前途的解决方案,用于在资源有限的环境中准确的结肠多片细分.
  • 介质特征蒸增强了对弱半监督细分任务的学习.
  • 这种方法有助于通过医疗成像中先进的AI改善结直肠癌的早期检测.