Skin Lesion Classification Using Weakly-supervised Fine-grained Method
Skin Lesion Classification Using Weakly-supervised Fine-grained Method
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DOI：
10.1109/icpr48806.2021.9412042
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
Xi Xue;S. Kamata;Daming Luo
中科院分区：
文献类型：
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作者：
Xi Xue;S. Kamata;Daming Luo
近年来，皮肤癌已成为最常见的癌症之一。在所有类型的皮肤癌中，黑色素瘤是最致命的一种，每年有许多人死于这种疾病。早期发现可以大大降低死亡率，挽救更多生命。皮肤病变是黑色素瘤和其他类型皮肤癌的早期症状之一。因此，早期准确识别各种皮肤病变具有重要意义。目前已有很多基于卷积神经网络（CNN）的皮肤病变分类研究，但很少涉及不同病变之间的相似性。例如，我们发现像黑色素瘤和痣这样的病变在外观上看起来很相似，这使得神经网络很难区分皮肤病变的类别。受细粒度图像分类的启发，我们提出了一种新的网络来准确区分每个类别。在本文中，我们设计了一个有效的模块，独特的区域建议模块（DRPM），从每幅图像中提取独特的区域。空间注意力和通道注意力都被用来丰富特征图，并引导网络以弱监督的方式关注突出显示的区域。另外，为了保证网络得到更好的结果，增加了两个预处理步骤。我们在ISIC 2017数据集上证明了所提出的方法的潜力。实验表明，该方法是有效的.
In recent years, skin cancer has become one of the most common cancers. Among all types of skin cancers, melanoma is the most fatal one and many people die of this disease every year. Early detection can greatly reduce the death rate and save more lives. Skin lesions are one of the early symptoms of melanoma and other types of skin cancer. So accurately recognizing various skin lesions in early stage is of great significance. There have been lots of existing works based on convolutional neural networks (CNN) to solve skin lesion classification but seldom do they involve the similarity among different lesions. For example, we find that some lesions like melanoma and nevi look similar in appearance which is hard for neural network to distinguish categories of skin lesions. Inspired by fine-grained image classification, we propose a novel network to distinguish each category accurately. In our paper, we design an effective module, distinct region proposal module (DRPM), to extract the distinct regions from each image. Spatial attention and channel-wise attention are both utilized to enrich feature maps and guide the network to focus on the highlighted areas in a weakly-supervised way. In addition, two preprocessing steps are added to ensure the network to get better results. We demonstrate the potential of the proposed method on ISIC 2017 dataset. Experiments show that our approach is effective and efficient.