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