性结肠炎,LAIR1和TOX2表达和结肠直肠癌 深度学习使用卷积神经网络的图像分类
Joaquim Carreras1, Giovanna Roncador2, Rifat Hamoudi3,4,5,6,7
1Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Japan.
Cancers
|January 8, 2025
概括
人工智能,特别是使用卷积神经网络 (CNN) 的深度学习,从组织图像中准确地分类性结肠炎和结直肠癌. 这种AI方法还可以识别与性结肠炎相关的关键免疫标记物 (LAIR1,TOX2).
科学领域:
- 在瘤学瘤学.
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 性结肠炎 (UC) 是一种慢性炎症性肠病,增加了结肠直肠癌 (CRC) 风险.
- 准确的UC和CRC组织学分类对于患者管理至关重要.
研究的目的:
- 用深度学习 (CNNs) 来分类UC,正常结肠和CRC的组织图像.
- 评估AI在区分疾病亚型和识别免疫标记物的实用性.
主要方法:
- 基于ResNet-18的CNN接受了UC,正常结肠和CRC病例的H&E染色图像的培训.
- 使用培训,验证和测试数据集评估图像分类性能.
- 使用可解释AI (Grad-CAM) 和免疫组织化学 (LAIR1,TOX2) 来分析图像特征和免疫微环境.
主要成果:
- 美国有线电视新闻网 (CNN) 实现了高分类准确率:UC的99.1%,CRC的99.8%,正常结肠的99.1%.
- 人工智能模型根据H&E,LAIR1和TOX2染色区分了需要类固醇和响应美沙的UC.
- 在需要类固醇的UC中,LAIR1的表达更高,而在隔离的淋巴卵泡中,TOX2的表达更低.
结论:
- 从组织学图像中分类UC和CRC,CNN显示出高效率.
- 在性结肠炎中,LAIR1和TOX2是显著的免疫瘤学标志物,有助于疾病的表征.
关键词:
图像网 图像网 图像网在ResNet-18网络网络中,腺癌瘤是一种腺癌.人工智能的人工智能是人工智能.计算机视觉 计算机视觉深度学习是一种深度学习.机器学习是机器学习.神经网络的神经网络的神经网络预先训练的模型模型.性结肠炎是一种更多相关视频
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