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

Uterus and Cervix01:18

Uterus and Cervix

1.5K
The uterus, commonly called the womb, is a vital reproductive organ in females designed to provide a nurturing environment for the implantation and growth of an embryo. It is shaped like a hollow pear and positioned between the urinary bladder and the rectum. The uterus's structure allows it to support and protect a developing fetus throughout pregnancy.
The uterus is securely anchored within the pelvic cavity by paired broad ligaments on either side. It is further stabilized by three pairs...
1.5K

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 manifestations and ultrasonographic features of lobular endocervical glandular hyperplasia: a retrospective study of 135 patients.

Annals of medicine·2026
Same author

Mitigating hallucinations in synthesized clinical texts to improve multimodal deep learning for dermatology.

Journal of biomedical informatics·2026
Same author

Multimodal Learning with Privileged Report Supervision for Generalizable Tuberculosis Detection on Chest Radiographs.

Journal of medical systems·2026
Same author

Oral Cancer Detection By Using Tabular Data Synthesis and Classification.

Proceedings ... ICDM workshops. IEEE International Conference on Data Mining·2026
Same author

Artificial Intelligence-Based Diagnosis of Kaposi Sarcoma Using Digital Photographs in Dark-Skinned Patients in Uganda.

JCO global oncology·2026
Same author

Multi-task Cross-modal Learning for Chest X-ray Image Retrieval.

ArXiv·2026

相关实验视频

Updated: Jul 24, 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

1.9K

从异质和部分标记图像数据集的深度子宫模型开发.

Anabik Pal1,2, Zhiyun Xue2, Sameer Antani2

  • 1SRM University, Amaravati, Guntur District, Andhra Pradesh, India, 522502.

Frontiers of ICT in Healthcare : proceedings of EAIT 2022. International Conference on Emerging Applications of Information Technology (7th : 2022 : Kolkata, India ; Online)
|July 3, 2023
PubMed
概括

这项研究开发了一种使用自主监督学习 (SSL) 和联合自主监督学习 (FSSL) 的自动化宫图像分类系统,以改善癌前检测,优于标准模型.

关键词:
宫图像分类 宫图像分类深度学习 (Deep Learning) 是一种深度学习.联合学习学习 (Federated Learning) 是一种联合学习.自主监督学习学习

更多相关视频

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
10:39

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

2.4K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.5K

相关实验视频

Last Updated: Jul 24, 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

1.9K
A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
10:39

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

2.4K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.5K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 宫癌是全球女性的主要健康问题.
  • 早期发现子宫癌前期癌症至关重要,但由于专家稀缺和解释变化而受到限制.
  • 需要自动化宫图像分类系统来协助专家.

研究的目的:

  • 使用异质和部分标记的宫图像数据集开发一个强大的预训练子宫模型.
  • 探索自主监督学习 (SSL) 和联合自主监督学习 (FSSL) 的应用,用于宫图像分析.
  • 通过自动化系统提高宫癌前期检测的准确性和可靠性.

主要方法:

  • 利用自主监督学习 (SSL) 在部分标记的宫图像数据集上预训练宫模型.
  • 实施联合自主监督学习 (FSSL) 来训练模型而不需要直接共享数据,解决隐私问题.
  • 微调了预训练的宫模型,以便在两个不同的数据集上进行任务特定的分类,并根据不同的标签标准进行分类.

主要成果:

  • 与ImageNet预训练模型相比,数据集特定的SSL预训练子宫模型在分类准确度上表现出2.5%的增加.
  • 将两个数据集的图像用于SSL的结合,进一步提高了分类准确率1.5%.
  • 与数据集特定的SSL模型相比,联合自主监督学习 (FSSL) 显示出更高的性能.

结论:

  • SSL和FSSL是从异质的,部分标记的数据集开发高性能子宫模型的有效方法.
  • 在尊重数据隐私和共享限制的同时,FSSL为协作模型开发提供了可行的解决方案.
  • 开发的自动化系统有可能增加宫癌前查和诊断方面的专家能力.