The Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China.
Ningbo First Hospital, Zhejiang University, Ningbo, 315010, China.
Comput Biol Med. 2022 Oct;149:105939. doi: 10.1016/j.compbiomed.2022.105939. Epub 2022 Aug 10.
Use of artificial intelligence to identify dermoscopic images has brought major breakthroughs in recent years to the early diagnosis and early treatment of skin cancer, the incidence of which is increasing year by year worldwide and poses a great threat to human health. Achievements have been made in the research of skin cancer image classification by using the deep backbone of the convolutional neural network (CNN). This approach, however, only extracts the features of small objects in the image, and cannot locate the important parts.
As a result, researchers of the paper turn to vision transformers (VIT) which has demonstrated powerful performance in traditional classification tasks. The self-attention is to improve the value of important features and suppress the features that cause noise. Specifically, an improved transformer network named SkinTrans is proposed.
To verify its efficiency, a three step procedure is followed. Firstly, a VIT network is established to verify the effectiveness of SkinTrans in skin cancer classification. Then multi-scale and overlapping sliding windows are used to serialize the image and multi-scale patch embedding is carried out which pay more attention to multi-scale features. Finally, contrastive learning is used which makes the similar data of skin cancer encode similarly so that the encoding results of different data are as different as possible.
The experiment is carried out based on two datasets, namely (1) HAM10000: a large dataset of multi-source dermatoscopic images of common skin cancers; (2)A clinical dataset of skin cancer collected by dermoscopy. The model proposed has achieved 94.3% accuracy on HAM10000 and 94.1% accuracy on our datasets, which verifies the efficiency of SkinTrans.
The transformer network has not only achieved good results in natural language but also achieved ideal results in the field of vision, which also lays a good foundation for skin cancer classification based on multimodal data. This paper is convinced that it will be of interest to dermatologists, clinical researchers, computer scientists and researchers in other related fields, and provide greater convenience for patients.
近年来，人工智能在皮肤科图像识别方面取得了重大突破，有助于提高皮肤癌的早期诊断和治疗水平。全球范围内，皮肤癌的发病率逐年上升，对人类健康构成了严重威胁。利用卷积神经网络（CNN）的深度骨干网络在皮肤癌图像分类方面的研究已经取得了一定的成果。然而，这种方法只能提取图像中小目标的特征，无法定位重要部分。
因此，本文的研究人员转向视觉转换器（VIT），它在传统分类任务中表现出了强大的性能。自注意力机制可以提高重要特征的数值，抑制产生噪声的特征。具体来说，提出了一种名为 SkinTrans 的改进型转换器网络。
为了验证其效率，采用了三步法。首先，建立了一个 VIT 网络来验证 SkinTrans 在皮肤癌分类中的有效性。然后，使用多尺度和重叠滑动窗口对图像进行序列化，并进行多尺度补丁嵌入，以更好地关注多尺度特征。最后，使用对比学习，使皮肤癌的相似数据编码相似，从而使不同数据的编码结果尽可能不同。
该实验基于两个数据集进行，分别为（1） HAM10000：一个多源常见皮肤癌皮肤镜图像的大型数据集；（2）通过皮肤镜收集的皮肤癌临床数据集。所提出的模型在 HAM10000 上的准确率达到了 94.3%，在我们的数据集上的准确率达到了 94.1%，验证了 SkinTrans 的效率。
转换器网络不仅在自然语言领域取得了良好的效果，在视觉领域也取得了理想的效果，这也为基于多模态数据的皮肤癌分类奠定了良好的基础。本文相信，它将引起皮肤科医生、临床研究人员、计算机科学家和其他相关领域研究人员的兴趣，为患者提供更大的便利。
