人工智能
卷积神经网络
计算机科学
散列函数
旋转(数学)
图像检索
深度学习
模式识别(心理学)
图像(数学)
计算机视觉
计算机安全
作者
Yilan Zhang,Fengying Xie,Xuedong Song,Yushan Zheng,Jie Liu,Juncheng Wang
标识
DOI:10.1016/j.media.2021.102301
摘要
Dermoscopic image retrieval technology can provide dermatologists with valuable information such as similar confirmed skin disease cases and diagnosis reports to assist doctors in their diagnosis. In this study, we design a dermoscopic image retrieval algorithm using convolutional neural networks (CNNs) and hash coding. A hybrid dilated convolution spatial attention module is proposed, which can focus on important information and suppress irrelevant information based on the complex morphological characteristics of dermoscopic images. Furthermore, we also propose a Cauchy rotation invariance loss function in view of the skin lesion target without the main direction. This function constrains CNNs to learn output differences in samples from different angles and to make CNNs obtain a certain rotation invariance. Extensive experiments are conducted on dermoscopic image datasets to verify the effectiveness and versatility of the proposed module, algorithm, and loss function. Experiment results show that the rotation-invariance deep hashing network with the proposed spatial attention module obtains better performance on the task of dermoscopic image retrieval.
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