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Updated: Jan 9, 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

通过使用人工智能进行细胞病理图像分析来加速前列腺癌检测.

Anandh Sam Chandra Bose¹, Chandran Srinivasan², Chandrasekaran Saravanakumar³

¹Department of Industrial Engineering, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia.

Microscopy research and technique

|December 11, 2025

概括

此摘要是机器生成的。

这项研究引入了一种混合深度学习模型,将CNN和视觉转换器结合起来,以准确诊断前列腺癌. 人工智能框架显著提高了检测率,为临床应用提供了一个有前途的工具.

关键词:

人工智能的人工智能是人工智能.计算机辅助诊断是指计算机辅助的诊断.基因病理学图像基因病理学图像前列腺癌是前列腺癌.视觉变压器视觉变压器

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科学领域:

在瘤学瘤学.
计算机科学计算机科学
医疗成像医学成像

背景情况:

前列腺癌是男性癌症死亡的主要原因,需要早期和准确的诊断.
手动组织病理学分析是黄金标准,但是劳动密集型,需要专业知识.
目前的诊断方法在速度和一致性方面面临挑战.

研究的目的:

开发和评估混合深度学习框架,以提高前列腺癌检测.
整合本地和全球特征提取以提高诊断准确度.
创建一个有效的AI模型,适合临床部署.

主要方法:

一个混合框架,结合了整体卷积神经网络 (CNN) 和视觉变压器 (ViT).
使用VGG-16,DenseNet-121和AlexNet的转移学习,以及微调的ViT.
整合了交叉注意力融合 (CAF) 模块和知识蒸 (KD) 模块,以实现功能集成和效率.
经过PANDA数据集的训练和测试,使用了诸如马校正和染色解卷等预处理技术.

主要成果:

拟议的混合模型实现了97.91%的准确性,超过了现有方法.
在真实阳性率 (TPR) 和真实阴性率 (TNR) 中显著改善,假阴性率 (FNR) 和假阳性率 (FPR) 降低.
废除研究证实了单个组件的有效性,特别是组合CNN,CAF和ViT.

结论:

混合深度学习模型为前列腺癌诊断提供了强大而准确的方法.
人工智能框架显示出加速诊断和及时患者干预的潜力.
该模型平衡了预测准确性与临床适用性计算效率的平衡.