使用卷积神经网络将患者数据集成到皮肤癌分类中卡塔尔世界杯8强波胆分析系统评论%A Höhn,Julia %A Hekler,Achim %A Krieghoff-Henning,Eva %A kaather,Jakob Nikolas %A Utikal,Jochen Sven %A Meier,Friedegund %A Gellrich,Frank Friedrich %A Hauschild,Axel %A French,Lars %A Schlager,Justin Gabriel %A Ghoreschi,Kamran %A Wilhelm,Tabea %A Kutzner,Heinz %A Heppt,Markus %A Haferkamp,Sebastian %A Sondermann,Wiebke %A Schadendorf,Dirk %A Schilling,Bastian %A Maron,Roman C %A Schmitt,Max %A Jutzi,Tanja %A Fröhling,Stefan %A Lipka,Daniel B %A Brinker,Titus Josef %+ Digital肿瘤生物标志物组(DBO)，国家肿瘤疾病中心(NCT)，德国癌症研究中心(DKFZ)， Im Neuenheimer Feld 460，海德堡，德国，49 62213219304,titus.brinker@nct-heidelberg.de %K皮肤癌分类%K卷积神经网络%K患者数据%D 2021 %7 2.7.2021 %9综述%J J Med Internet Res %G英文%X近年来，使用卷积神经网络(cnn)对皮肤癌分类的准确性有了实质性的提高。在单幅图像的分类任务方面，cnn的表现与皮肤科医生相当或更好。然而，在临床实践中，皮肤科医生也使用数字化图像中存在的视觉方面以外的其他患者数据，进一步提高了他们的诊断准确性。最近，几项试点研究调查了将不同亚型的患者数据整合到基于cnn的皮肤癌分类器中的效果。目的:本系统综述了目前关于图像特征和患者数据融合信息对基于cnn的皮肤癌图像分类性能影响的研究。本研究旨在通过评估所使用的患者数据类型，非图像数据编码和与图像特征合并的方式，以及整合对分类器性能的影响，探索该领域的研究潜力。方法:筛选Google Scholar、PubMed、MEDLINE和ScienceDirect发表的同行评议的英文研究，这些研究涉及在基于cnn的皮肤癌分类中整合患者数据。 The search terms skin cancer classification, convolutional neural network(s), deep learning, lesions, melanoma, metadata, clinical information, and patient data were combined. Results: A total of 11 publications fulfilled the inclusion criteria. All of them reported an overall improvement in different skin lesion classification tasks with patient data integration. The most commonly used patient data were age, sex, and lesion location. The patient data were mostly one-hot encoded. There were differences in the complexity that the encoded patient data were processed with regarding deep learning methods before and after fusing them with the image features for a combined classifier. Conclusions: This study indicates the potential benefits of integrating patient data into CNN-based diagnostic algorithms. However, how exactly the individual patient data enhance classification performance, especially in the case of multiclass classification problems, is still unclear. Moreover, a substantial fraction of patient data used by dermatologists remains to be analyzed in the context of CNN-based skin cancer classification. Further exploratory analyses in this promising field may optimize patient data integration into CNN-based skin cancer diagnostics for patients’ benefits. %M 34255646 %R 10.2196/20708 %U //www.mybigtv.com/2021/7/e20708 %U https://doi.org/10.2196/20708 %U http://www.ncbi.nlm.nih.gov/pubmed/34255646