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LLM2image:使用病理图像和语义信息来准确诊断腹病毒的新框架

Liangliang Liu¹, Zizhao Niu¹, Fengjie Zhao¹

¹Henan Agricultural University, Zhengzhou, 460045, Henan, PR China.

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

|February 24, 2026

概括

这项研究介绍了LLM2image,这是一种用于在动物组织病理学中诊断腹病毒的新型AI框架. 它将视觉数据与文本描述相结合,实现兽医水平的准确性,以改善疾病控制.

科学领域:

兽医病理学兽医病理学
人工智能的人工智能
计算生物学计算生物学

背景情况:

在兽医中,对腹病毒的准确诊断依赖于组织病理学图像.
当前的深度学习方法往往忽视临床语义,并与有限的数据作斗争.
整合视觉和文本信息对于提高诊断准确性至关重要.

研究的目的:

开发一个多式模式框架,LLM2image,以改善病理图像分类.
利用大型语言模型 (LLM) 来生成语义丰富的文本描述.
为了提高AI驱动疾病诊断的准确性和可解释性.

主要方法:

一个像素级MLP编码器用于图像表示.
GPT-4.0用于生成类特定的病理描述.
一个采用多头注意力进行特征对齐的交叉模式融合变压器.

主要成果:

在猪肠道图像 (健康,ETEC,PDCoV,PoRV) 上获得了89.51%的准确性.
在PAIP2020结直肠癌数据集上,证明了87.42%的准确性.
通过LLM生成的文本显著提高了分类准确性和可解释性.

结论:

通过整合视觉和文本数据,LLM2image提供了一种强大的方法来对病态图像进行分类.
该框架实现了与经验丰富的兽医相美的诊断性能.
作为轻量级的Android应用程序的部署可以为现实应用程序提供快速的离线诊断.

关键词:

在 GPT 中,GPT 必须是 GPT.组织病理图像组织病理图像大型语言模型.像素特征的特征是像素的特征.语义信息是语义信息.

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