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

Search research articles

相关实验视频

Updated: Mar 11, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

人工智能病理学公共数据库:系统审查

Aniketh Reddy Chinnachinnanagari¹, Shyam Sundar Debsarkar¹, V B Surya Prasath²

¹Department of Computer Science, University of Cincinnati, Cincinnati, OH, 45221, USA.

Journal of imaging informatics in medicine

|March 10, 2026

概括

此摘要是机器生成的。

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

SpaDiffHis: Sparse-point Guided Diffusion for Histopathology Image Synthesis with Contrastive Learning.

IEEE journal of biomedical and health informatics·2026

Same author

Cross-Architecture Knowledge Distillation for Histopathological Image Analysis.

IEEE access : practical innovations, open solutions·2026

Same author

Automated Eosinophil Quantification Using Deep Learning to Predict Therapy Escalation in Pediatric Ulcerative Colitis.

Clinical and translational gastroenterology·2026

Same author

End-to-End Multimodal Multiple Instance Learning for Cancer Histopathology Classification with Dual-Attention Fusion.

Journal of medical systems·2026

Same author

A multi-expert deep learning framework with LLM-guided arbitration for multimodal histopathology prediction.

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

Same author

Multimodal Ensemble Fusion Deep Learning Using Histopathological Images and Clinical Data for Glioma Subtype Classification.

IEEE access : practical innovations, open solutions·2025

Same journal

Kolmogorov-Arnold Guided Local-Global Attention for Medical Image Classification.

Journal of imaging informatics in medicine·2026

Same journal

Artificial Intelligence-Assisted Inner Ear Computed Tomography Analysis: Radiomics-Based Comparison of Affected and Unaffected Ears in Idiopathic Sudden Sensorineural Hearing Loss.

Journal of imaging informatics in medicine·2026

Same journal

High Adoption, Higher Expectations: A Cross-Sectional Survey of Radiologist Engagement with Artificial Intelligence in the United Arab Emirates.

Journal of imaging informatics in medicine·2026

Same journal

Complex-valued Multi-scale Hybrid Attention Network for Fast MRI via Sparsified Data Learning.

Journal of imaging informatics in medicine·2026

Same journal

Automatic Phase and Sequence Identification in Gd-EOB-DTPA-Enhanced Liver MRI Using Deep Convolutional and Sequential Learning.

Journal of imaging informatics in medicine·2026

Same journal

Ultrasound-Based AI in Predicting Hormone Receptor Status in Breast Cancer: Is "Digital Biopsy" Possible.

Journal of imaging informatics in medicine·2026

查看所有相关文章

这项研究审查了151个公开的人体病理学数据集用于人工智能 (AI) 研究. 它将数据集分类并分析关键数据集,以帮助AI模型开发用于癌症诊断和治疗.

科学领域:

数字病理学数字病理学
计算性组织病理学
人工智能在医学中的应用

背景情况:

组织病理学对于疾病诊断至关重要,特别是癌症,影响患者的治疗.
数字病理学中的人工智能 (AI) 提高了诊断速度,准确性和可扩展性.
结构良好的,注释的组织病理学数据集对于在病理学中推进AI至关重要.

研究的目的:

为人工智能和机器学习研究提供公开可用的基因病理学数据集的全面概述.
根据各种参数对数据集进行分类,并分析流行的例子.
引导研究人员选择合适的数据集,用于开发AI模型的计算病理学.

主要方法:

对公开可用的基因病理学数据集进行系统审查.
识别和收集151个不同的组织类型和癌症相关数据集.
数据集按患者数量,器官,染色,放大,扫描仪,大小,收集方法,年份和分辨率进行分类.
对关键数据集的分析,如CAMELYON,TUPAC,MIDOG,MoNuSeg和BreakHis. 这样的数据集.

主要成果:

编制了151个适合人工智能研究的组织病理学数据集的精选列表.
数据集被全面分类,详细说明其特征和潜在应用.

关键词:

人工智能的人工智能是人工智能.大数据就是大数据.分类分类分类分类.计算病理学计算病理学数据标准化数据标准化数据集是一个数据集.深度学习是一种深度学习.数字病理学数字病理学审查审查审查审查分段化分段化分段化分段化整个幻灯片成像成像系统.

相关实验视频

Last Updated: Mar 11, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

分析突出了流行的数据集及其在计算病理学中的特定用途.

确定了当前数据集可用性的缺口,特别是用于多式联运数据集成.

结论:

本综述为计算病理学研究人员提供了有价值的资源,有助于为AI模型开发选择数据集.
标准化图像和解决数据缺口,特别是多式联网数据,将提高AI模型的通用性和可重复性.
促进协作和改善数据集的可访问性是推动人工智能驱动的病理诊断的关键.