TY -的AU -霍恩,茱莉亚盟——Hekler Achim AU - Krieghoff-Henning,伊娃盟凯丝,雅各布·尼古拉斯盟——Utikal Jochen斯文AU -迈耶,Friedegund AU - Gellrich,弗兰克•弗里德里希•AU - Hauschild Axel盟——法国佬司AU - Schlager贾斯汀Gabriel AU - Ghoreschi Kamran AU -威廉,Tabea盟——Kutzner亨氏盟——Heppt马库斯盟——Haferkamp塞巴斯蒂安盟——Sondermann Wiebke盟——Schadendorf Dirk AU -先令,巴斯蒂安·AU -马龙,罗马C AU -施密特,马克斯AU -朱迪斯,挚友盟——FrohlingStefan盟利,Daniel B非盟- Brinker提图斯约瑟夫PY - 2021 DA - 2021/7/2 TI -皮肤癌患者数据集成到使用卷积神经网络分类:系统回顾乔- J地中海互联网Res SP - e20708六世- 23 - 7 KW -皮肤癌症分类KW -卷积神经网络KW -病人数据AB -背景:近年来目睹皮肤癌的准确性在很大程度上改善分类使用卷积神经网络(cnn)。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。将皮肤癌分类、卷积神经网络、深度学习、病变、黑色素瘤、元数据、临床信息和患者数据进行组合。结果:11篇文献符合纳入标准。 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. SN - 1438-8871 UR - //www.mybigtv.com/2021/7/e20708 UR - https://doi.org/10.2196/20708 UR - http://www.ncbi.nlm.nih.gov/pubmed/34255646 DO - 10.2196/20708 ID - info:doi/10.2196/20708 ER -