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皮肤镜暗角物件去除:朋友还是敌人?

Samuel William Pewton¹, Bill Cassidy¹, Connah Kendrick¹

¹Department of Computing and Mathematics, Faculty of Science and Engineering, Manchester Metropolitan University, Chester Street, Manchester, M1 5GD, UK.

Computer methods and programs in biomedicine

|December 29, 2023

概括

将合成暗角器件引入训练数据中,可以改善皮肤癌分类的深度学习模型. 这有助于模型忽略文物,提高了检测黑色素瘤的真负率.

科学领域:

皮肤病学皮肤病学
人工智能的人工智能
医疗成像医学成像

背景情况:

皮肤镜图像中的工件对皮肤癌症的分类构成了挑战.
之前的去除文物方法由于有限的标记数据而产生了不确定的结果.

研究的目的:

调查暗角文物对深度学习对皮肤癌的二进制分类的影响.
评估策略,以减轻人工物对模型性能的影响.

主要方法:

标记了10250张皮肤病变图像,创建了一个平衡的数据集.
使用了一个没有文物的训练组 (6126张图像) 和一个带有文物的测试组 (4124张图像).
通过绘制和合成生成的文物实例进行了实验.

主要成果:

将合成暗角工件叠加在训练组上,提高了模型性能,特别是真负率.
深度学习模型学会了在训练数据中存在时忽略文物.
开发了一种使用根平均平方亮度强度的新型热图量化方法.

结论:

拟议的工件方法可以减轻暗角工件对模型性能的影响.
新的热图分析有助于理解网络焦点和性能指标之间的关系.

关键词:

黑暗的角落文物艺术品这就是ISICIC.黑色素瘤是一种黑色素瘤.皮肤癌是一种皮肤癌.维尼耶特是指一个维尼耶特.

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