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计算机视觉基础模型在内镜:在口腔癌的概念证明

Alberto Paderno^1,2, Anita Rau³, Nikita Bedi⁴

¹Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Milan, Italy.

The Laryngoscope

|June 8, 2024

概括

视力转换器嵌入器可以准确地区分瘤与正常的口腔关组织. 这种计算机视觉方法显示了增强粘膜病变内镜诊断的前景.

科学领域:

医学成像分析分析医学成像分析
计算机视觉在医疗保健中的应用
耳鼻喉科的研究研究.

背景情况:

区分瘤与正常口组织对于早期癌症检测至关重要.
传统的内镜评估可能是主观的,并可能从客观的计算工具中受益.

研究的目的:

评估视觉变压器衍生图像嵌入的有效性,用于分类口腔关组织.
探索计算机视觉 (CV) 基础模型在医学诊断中的潜力.

主要方法:

使用自我监督的视觉转换器模型 (DINOv2) 来从内镜图像中生成嵌入.
支持矢量机 (SVM) 分类器在这些嵌入物上接受了培训,用于瘤与正常组织的分类.
在38个视频中的327个图像补丁上使用80-20列车验证分割验证性能.

主要成果:

该分类模型实现了高准确度 (92%) 和精度 (89%).
记录了完美的回忆 (100%) 和94%的F1得分.
接收机操作特征曲线显示出强大的区分能力,曲线下的面积 (AUC) 为0.96.

结论:

视力变压器衍生的嵌入物有效地将瘤与正常口腔关组织区分开来.
像DINOv2这样的CV基础模型显示了在内镜粘膜病变评估中增加诊断精度的可行性.
这项技术有可能提高耳鼻喉科的诊断准确性.

关键词:

人工智能的人工智能是人工智能.计算机视觉计算机视觉通过内镜检查 (endoscopy) 进行内镜检查基础模型基础模型这是口腔喉癌.

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