人工智能在消化道肿瘤医学图像数据处理的应用

doi:10.16150/j.1671-2870.2025.06.005

摘要/Abstract

摘要：

近年来，随着人工智能（artificial intelligence， AI）算法模型的快速发展，出现了以图像分类为主要功能的卷积神经网络（convolutional neural network， CNN）算法、决策分类的决策树和支持向量机模型、提高模型识别精准性的注意力机制模型、完成图像中病灶识别定位的目标检测模型，以及实现图像中病灶精准分割的语义分割和实例分割模型等。AI的CNN与医学影像深度融合，显著提高了疾病诊断的效率和精准率。随着数字医学水平提高，AI在疾病诊疗路径中图像数据结合点进一步拓宽。除了传统的放射学、超声学、内镜学和病理学等图像，手术切除标本图像和类器官图像等非经典图像也逐步被纳入了AI研究范畴。AI的深度介入有助于解码多维数据中的隐性信息，不断改变疾病的诊治模式。可以预见，未来AI算法与各类疾病诊疗设备镶嵌整合，将成为疾病诊断、治疗乃至发展趋势预判的有力工具。

关键词: 传统医学图像, 手术标本, 类器官, 人工智能

Abstract:

In recent years, with the rapid development of artificial intelligence (AI) algorithm models, a variety of approaches have emerged, such as convolutional neural networks (CNN) algorithms mainly for image classification, decision trees and support vector machine models for decision classification, attention mechanism models for impro-ving identification accuracy, object detection models for lesion identification and localization in images, and semantic segmentation and instance segmentation models for precise lesion segmentation in images. The deep integration of AI-based CNN with medical imaging has significantly improved the efficiency and accuracy of disease diagnosis. With the improvement of digital medicine, the integration points of image data with AI in disease diagnosis and treatment pathways have further expanded. Besides traditional images, (radiology, ultrasonography, endoscopy, pathology), non-classical images, such as surgical resection specimen images and organoid images, are gradually incorporated into the scope of AI research. The deep involvement of AI helps decode hidden information in multi-dimensional data, conti-nuously transforming disease diagnosis and treatment patterns. In the foreseeable future, the integration of AI algorithms with various disease diagnosis and treatment devices will become powerful tools for disease diagnosis, treatment, and even prediction of development trends.

Key words: Traditional medical images, Surgical specimens, Organoids, Artificial intelligence

中图分类号:

R445
R735

杨蕊馨, 于颖彦. 人工智能在消化道肿瘤医学图像数据处理的应用[J]. 诊断学理论与实践, 2025, 24(06): 605-612.

YANG Ruixin, YU Yingyan. Application of artificial intelligence in medical image data processing for digestive tract tumors[J]. Journal of Diagnostics Concepts & Practice, 2025, 24(06): 605-612.

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AI 模型	算法	功能	参数
CNN	VGG、Inception、ResNet、InceptionResNetV2、DenseNet、EfficientNet、Xception、MobileNet	图像分类	精确度、准确率、召回率、特异性、阳性预测值、阴性预测值、受试者操作特征曲线和曲线下面积
SVM	SVM、S3VM	图像聚类	精确度、准确率、召回率、特异度、阳性预测值、阴性预测值、受试者操作特征曲线和曲线下面积
注意力机制	软注意力、硬注意力	注意力	提高AI模型的训练效果
目标检测	YOLO、SSD、CenterNet、EfficientNet	病变检测	mAP
语义分割	FCN、SegNet、U-Net、V-Net、DeepLab、PSPNet	病变分割	mIoU
实例分割	掩码 R-CNN、级联掩码 R-CNN、YOLACT、CondInst、SOLO	单一病变分割	mIoU

应用	AI 模型	图像类型	功能	评估参数
内镜图像	SSD	彩色内镜图像	早期胃癌检测	mAP
	DeepLab v3+	白光内镜图像	上消化道肿瘤评估	mIoU
	UNet++	彩色内镜图像或白光内镜图像	早期胃癌边缘勾画	mIoU
	FCN-8	白光内镜图像	结直肠息肉分割	mIoU
	EfficientNetB7	白光内镜图像	胃癌与非胃癌、幽门螺旋杆菌感染与非幽门螺旋杆菌感染的分类，淋巴结转移的预测	精确度、准确率、召回率、AUC
放射影像	SVM	CT	淋巴结转移的预测	mIoU
	Faster R-CNN	CT	淋巴结转移的预测	mAP
	R-CNN	MRI	手术目标勾画	mIoU
手术切除标本图像	RFB-SSD ResNet50-PSPNet	胃切除标本图像	病变检测与分割，淋巴结转移的预测	mAP、mIoU
病理图像	InceptionV3	全切片数字图像	正常、慢性胃炎和肠型胃癌的分类	精确度、准确率、召回率、AUC
	YOLOv4	全切片数字图像	胃癌诊断	mAP
	ResNet18	全切片数字图像	幽门螺旋杆菌的预测	精确度、准确率、召回率、AUC
类器官图像	SSD	白光类器官图像	类器官的检测与追踪	mAP
	Faster R-CNN	白光类器官图像	人体肠道类器官的定位与量化	mAP
	CBAM-YOLOv3	白光类器官图像	类器官活力评估	mAP
	U-Net、SVM	白光类器官图像	结直肠癌类器官亚型分类	mAP、mIoU