RD Dataset

** RD DATASET ** RD dataset was created by the images from the melanoma community on the internet (_{https://reddit.com/r/melanoma}). Consecutive images were included using a python library (_{https://github.com/aliparlakci/bulk-downloader-for-reddit}) from Jan 25, 2020, to July 30, 2021. The ground truth was voted by four dermatologists and one plastic surgeon while referring to the chief complaint and brief history. A total of 1,282 images (1,201 cases) were finally included. Because of the deleted cases by users, the links of 860 cases are valid in July 2021. <br> 1. RD_RAW.xlsx The download links and ground truth of the RD dataset are included in this excel file. In addition, the raw data of the AI (Model Dermatology Build2021 - https://modelderm.com) and 32 laypersons were included. <br> 2. v1_public.zip "v1_public.zip" includes the 1,282 lesional images (full-size). The 24 images that were excluded from the study are also available. <br> 3. v1_private.zip is not available here. Wide field images are not available here. If the archive is needed for research purpose, please email to Dr. Han Seung Seog ([e-mail]) or Dr Cristian Navarrete-Dechent ([e-mail]). <br> References - The Degradation of Performance of a State-of-the-art Skin Image Classifier When Applied to Patient-driven Internet Search - Scientific Report (in-press) <br> <br> ** Background normal test with the ISIC images ** ISIC dataset (_{https://www.isic-archive.com}; Gallery -&gt; 2018 JID Editorial images; 99 images; ISIC_0024262 and ISIC_0024261 are identical images and ISIC_0024262 was skipped) was used for the background normal test. We defined 10% area rectangle crop to “specialist-size crop”, and 5% area rectangle crop to “layperson-size crop” a) S-crops.zip: specialist-size crops<br> Format: CROPNO_AGE(0~99)_GENDER(1=male,0=female)[m]_FILENAME.png b) L-crops.zip: layperson-size crops<br> Format: CROPNO_AGE(0~99)_GENDER(1=male,0=female)[m]_FILENAME.png c) result_S.zip: Background normal test result using the specialist-size crops<br> d) result_L.zip; Background normal test result using the layperson-size crops <br> Reference - Automated Dermatological Diagnosis: Hype or Reality? - https://doi.org/10.1016/j.jid.2018.04.040 - Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement - https://doi.org/10.1016/j.jid.2020.06.040**RD数据集** RD数据集的素材来源于互联网黑色素瘤社区（_{https://reddit.com/r/melanoma}）。本数据集通过Python库（_{https://github.com/aliparlakci/bulk-downloader-for-reddit}）于2020年1月25日至2021年7月30日期间批量采集相关连续图像。数据集的真值标签由四名皮肤科医师与一名整形外科医师结合患者主诉及简要病史共同投票确定。最终共纳入1282张图像，对应1201个病例；因部分用户删除原帖，截至2021年7月时，仍有860个病例的下载链接有效。 1. RD_RAW.xlsx 该Excel文件包含RD数据集的下载链接与真值标签，同时收录了人工智能模型（Model Dermatology Build2021，https://modelderm.com）以及32名非专业人士的标注原始数据。 2. v1_public.zip 该压缩包包含全部1282张皮损图像（全尺寸版本），同时附赠研究中剔除的24张图像。 3. v1_private.zip 本页面未提供该压缩包，宽视野图像亦无法获取。若因科研用途需要该归档文件，请致信韩昇锡博士（[e-mail]）或克里斯蒂安·纳瓦雷特-德申特博士（[e-mail]）。 参考文献 - 《面向患者的互联网搜索场景下前沿皮肤图像分类器性能退化研究》——已录用科学报告 **基于ISIC数据集的背景正常对照测试** 本测试采用ISIC数据集（International Skin Imaging Collaboration, ISIC，_{https://www.isic-archive.com}；图库→2018年JID编辑图像；共99张图像；其中ISIC_0024262与ISIC_0024261为重复图像，故剔除ISIC_0024262）。我们将面积占比10%的矩形裁剪区域定义为「专业医师视角裁剪区」，面积占比5%的矩形裁剪区域定义为「非专业人士视角裁剪区」。 a) S-crops.zip：专业医师视角裁剪集 文件命名格式：CROPNO_AGE(0~99)_GENDER(1=男性,0=女性)[m]_FILENAME.png b) L-crops.zip：非专业人士视角裁剪集 文件命名格式：CROPNO_AGE(0~99)_GENDER(1=男性,0=女性)[m]_FILENAME.png c) result_S.zip：采用专业医师视角裁剪集完成的背景正常对照测试结果 d) result_L.zip：采用非专业人士视角裁剪集完成的背景正常对照测试结果 参考文献 - 《自动化皮肤病诊断：噱头还是现实？》——https://doi.org/10.1016/j.jid.2018.04.040 - 《皮肤病学领域的多分类人工智能：进展显著仍有改进空间》——https://doi.org/10.1016/j.jid.2020.06.040