• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

Reply to the letter to the Editor "Reply to 'Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists' by H. A. Haenssle et al. " by L. Oakden-Rayner.

作者信息

Haenssle H A, Fink C, Uhlmann L

出版信息

Ann Oncol. 2019 Feb;30(2):339. doi: 10.1093/annonc/mdy520. Epub 2019 Dec 24.

DOI:10.1093/annonc/mdy520
PMID:32089167
Abstract
摘要

相似文献

1
Reply to the letter to the Editor "Reply to 'Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists' by H. A. Haenssle et al. " by L. Oakden-Rayner.对L. 奥克登 - 雷纳所写的《致编辑的信：对H. A. 海恩斯勒等人〈人与机器：深度学习卷积神经网络与58位皮肤科医生在皮肤镜下黑色素瘤识别中的诊断性能对比〉的回复》的回复
Ann Oncol. 2019 Feb;30(2):339. doi: 10.1093/annonc/mdy520. Epub 2019 Dec 24.
2
Reply to the letter to the Editor "Reply to 'Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists' by H. A. Haenssle et al. " by L. Oakden-Rayner.对L. Oakden-Rayner所写的致编辑的信《对H. A. Haenssle等人的〈人与机器：深度学习卷积神经网络与58位皮肤科医生在皮肤镜下黑色素瘤识别中的诊断性能比较〉的回复》的回复
Ann Oncol. 2019 Feb 1;30(2):130e. doi: 10.1093/annonc/mdy520.
3
Reply to the letter to the editor 'Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists' by H. A. Haenssle et al.对H. A. 海恩斯勒等人致编辑的信《人与机器：深度学习卷积神经网络与58位皮肤科医生在皮肤镜下黑色素瘤识别中的诊断性能对比》的回复
Ann Oncol. 2019 May 1;30(5):854-857. doi: 10.1093/annonc/mdz015.
4
Reply to 'Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists' by Haenssle et al.对Haenssle等人所著的《人与机器的对抗：深度学习卷积神经网络与58位皮肤科医生在皮肤镜下黑色素瘤识别中的诊断性能对比》的回复
Ann Oncol. 2019 May 1;30(5):854. doi: 10.1093/annonc/mdy519.
5
Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.人机大战：深度学习卷积神经网络与 58 位皮肤科医生诊断黑色素瘤皮肤镜图像的对比研究
Ann Oncol. 2018 Aug 1;29(8):1836-1842. doi: 10.1093/annonc/mdy166.
6
Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions.人机大战续集：在更接近实际情况下，与 96 名皮肤科医生相比，市场认可的卷积神经网络在分类广泛的皮肤病变方面的表现。
Ann Oncol. 2020 Jan;31(1):137-143. doi: 10.1016/j.annonc.2019.10.013.
7
Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.人工智能及其对皮肤科医生在皮肤镜黑色素瘤图像分类中准确性的影响：基于网络的调查研究。
J Med Internet Res. 2020 Sep 11;22(9):e18091. doi: 10.2196/18091.
8
Dermoscopic diagnostic performance of Japanese dermatologists for skin tumors differs by patient origin: A deep learning convolutional neural network closes the gap.日本皮肤科医生对不同来源患者的皮肤肿瘤进行皮肤镜诊断的表现存在差异：深度学习卷积神经网络缩小了差距。
J Dermatol. 2021 Feb;48(2):232-236. doi: 10.1111/1346-8138.15640. Epub 2020 Oct 15.
9
Reply to the letter to the editor 'What type of man against machine?' by H. Smith.对H. 史密斯所写的致编辑的信《何种人机对抗？》的回复。
Ann Oncol. 2018 Sep 1;29(9):2024-2025. doi: 10.1093/annonc/mdy237.
10
Deep learning-based, computer-aided classifier developed with dermoscopic images shows comparable performance to 164 dermatologists in cutaneous disease diagnosis in the Chinese population.基于深度学习、利用皮肤镜图像开发的计算机辅助分类器，在中国人群的皮肤疾病诊断中，表现出与164名皮肤科医生相当的性能。
Chin Med J (Engl). 2020 Sep 5;133(17):2027-2036. doi: 10.1097/CM9.0000000000001023.