一个具有部分标签训练的多分辨率卷积神经网络,用于注释反射共焦显微镜皮肤图像
Alican Bozkurt1, Kivanc Kose2, Christi Alessi-Fox3
1Northeastern University, Boston, MA, USA.
概括
一个新的嵌套编码解码器网络有助于通过自动注释反射共焦显微镜 (RCM) 图像来诊断皮肤癌. 这种深度学习方法提高了准确性,并加快了对RCM分析的临床培训.
科学领域:
- 医学图像分析分析
- 计算病理学计算病理学
- 皮肤病学中的人工智能
背景情况:
- 皮肤癌,特别是黑色素瘤,是全球重要的健康问题.
- 反射共聚焦显微镜 (RCM) 为皮肤癌诊断提供非侵入性,高分辨率的成像.
- 由于复杂性,低对比度,变异性,解释RCM图像具有挑战性,并且需要广泛的专家培训.
研究的目的:
- 开发一种自动化方法,用于在人类皮肤的RCM图像中注释关键诊断模式.
- 用RCM提高皮肤癌诊断的准确性和效率.
- 促进临床培训和采用RCM技术.
主要方法:
- 设计了一种新的多分辨率"嵌套编码器-解码器"卷积网络架构.
- 实施了选择性损失函数来处理部分标记的图像.
- 该网络在大型 (12k × 12k像素),部分标记的黑色素瘤可疑皮肤病变RCM图像上进行了训练和验证.
主要成果:
- 开发的网络在RCM图像中自动注释诊断形态模式时实现了高灵敏度和特异性.
- 该系统为未标记的图像部分提供了一致的注释.
- 这种方法有效地处理了像大图像大小,模式尺度差异和阶级不平衡等挑战.
结论:
- 嵌套的编码器解码器网络为自动RCM图像注释提供了有效的工具,有助于皮肤癌诊断.
- 这项技术可以显著减少RCM解释所需的时间和专业知识,加速临床采用.
- 多分辨率深度网络架构在生物医学图像分析中可能具有更广泛的应用.
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