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在囊镜图像中基于人工智能的膀癌的分类和细分

Won Ku Hwang¹, Seon Beom Jo¹, Da Eun Han¹

¹Department of Urology, Korea University Guro Hospital, Korea University College of Medicine, Seoul 08308, Republic of Korea.

|January 11, 2025

概括

深度学习模型VGG19和Deeplab v3+在从囊镜图像中对膀癌的分类和细分方面显示出高准确性,这可能有助于改善诊断并减少复发.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
在瘤学瘤学.

背景情况:

囊镜对于膀癌的诊断至关重要,但与模两可的病变,如瘤 in situ (CIS) 斗争.
识别微妙病变的局限性导致膀癌复发率高.
深度学习显示了增强泌尿病学医学图像分析的前景.

研究的目的:

应用VGG19和Deeplab v3+深度学习模型来分类和细分膀癌的囊镜图像.
评估这些模型在识别癌症病变及其形态方面的表现.

主要方法:

利用772名患者的囊囊镜图像数据集,经验丰富的泌尿科医生对其进行了注释.
开发了一种VGG19模型,根据形态学和病理学对病变进行分类.
使用Deeplab v3+模型对不同膀癌形态进行细分.
使用稀疏的分类交叉和子系数损失函数,分别用于分类和细分.

主要成果:

VGG19分类模型的准确度达到0.912.
深度实验室v3+细分模型实现了0.833的交叉与联盟 (IoU) 和0.951.95的二进制准确性.
视觉检查证实了Deeplab v3+细分和专家注释之间的高度一致.

结论:

VGG19和Deeplab v3+在从囊泡镜图像中对膀癌症进行分类和细分方面表现出强的性能.
这些深度学习模型为膀癌研究提供了宝贵的工具.
这些模型显示了帮助临床医生更有效地诊断膀癌的潜力.

关键词:

人工智能的人工智能是人工智能.膀癌:膀癌是一种癌症.囊镜检查囊镜检查深度学习是一种深度学习.

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