Search research articles

相关概念视频

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

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

Multimodal Federated Learning in Healthcare: A Review.

Journal of healthcare informatics research·2026

Same author

Toward Patient-Specific Partial Point Cloud to Surface Completion for Pre to Intra-operative Registration in Image-Guided Liver Interventions.

Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference)·2026

Same author

Evaluation of Intra-operative Patient-specific Methods for Point Cloud Completion for Minimally Invasive Liver Interventions.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same author

Investigating the Domain Adaptability of General-Purpose Foundation Models for Left Atrium Segmentation from MR Images.

Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference)·2026

Same author

Studying Perfusion Effects on Heat Transfer in Tissue-Mimicking Phantoms for Cardiac Ablation: A Preliminary Experimental Investigation.

Journal of fluid flow, heat and mass transfer·2026

Same author

Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images.

Proceedings of SPIE--the International Society for Optical Engineering·2026

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

Search research articles

相关实验视频

Updated: Jun 16, 2025

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

在噪音标签中改善医疗图像分类,仅使用自主监督预训.

Bidur Khanal¹, Binod Bhattarai², Bishesh Khanal³

¹Center for Imaging Science, RIT, Rochester, NY, USA.

Data engineering in medical imaging : first MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. DEMI (Workshop) (1st : 2023 : Vancouver, B.C.)

|August 15, 2024

概括

此摘要是机器生成的。

自主监督学习预训练改善了在有噪音标签的医疗图像上训练的深度学习模型. 这种方法增强了特征提取和分类的稳定性,这对于准确的医学图像分析至关重要.

关键词:

功能提取特性提取标签噪声标签噪声学习与杂的标签学习.医学图像分类医学图像分类自主监督的预训.热身的障碍物是一个障碍.

更多相关视频

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

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

Published on: October 10, 2018

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

相关实验视频

Last Updated: Jun 16, 2025

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

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

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

Published on: October 10, 2018

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

科学领域:

医疗图像分析医学图像分析
深度学习是一种深度学习.
计算机视觉计算机视觉计算机视觉

背景情况:

噪音标签降低了深度学习模型的性能,因为它们会导致过拟合和损坏功能提取器.
对比的自我监督预训练可以提高自然图像的分类与噪音标签.
自主监督预训对有噪音标签的医学图像分类的影响仍然未被探索.

研究的目的:

调查对比和借口基于任务的自我监督预训的有效性,用于用噪音标签对医疗图像进行分类.
评估自然图像数据集中成功的自主监督预训方法是否适用于医学成像.

主要方法:

使用对比和借口基于任务的自我监督学习来预训练模型.
应用预训练以初始化医疗数据集 (NCT-CRC-HE-100K和COVID-QU-Ex) 的深度学习分类模型,使用诱导噪音标签.

主要成果:

自主监督预训有效地改善了医疗图像分类任务中的特征学习.
与标准训练相比,初始化的模型显示了对噪音标签的增强强性.

结论:

自主监督预训是减轻医疗图像分析中噪音标签负面影响的可行策略.
这种方法有望提高深度学习模型在不完善数据的临床环境中的可靠性.