The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset. We collected dermatoscopic images from different populations, acquired and stored by different modalities. The final dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions: Actinic keratoses and intraepithelial carcinoma / Bowen s disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemo

神经网络的训练旨在实现色素性皮肤病变的自动诊断，然而，现有的皮肤镜图像数据集规模有限且缺乏多样性，这对训练过程造成了阻碍。为解决此问题，我们发布了 HAM10000（“人机对抗，包含10000张训练图像”）数据集。该数据集汇集了来自不同人群、由不同方式获取和存储的皮肤镜图像。最终数据集包含10015张皮肤镜图像，这些图像可作为学术机器学习训练集。病例涵盖了色素性病变领域所有重要诊断类别的代表性样本：包括日光性角化病和上皮内癌/鲍文病（akiec）、基底细胞癌（bcc）、良性角化病样病变（包括日光性雀斑/脂溢性角化病以及扁平苔藓样角化病，bkl）、皮肤纤维瘤（df）、黑色素瘤（mel）、黑色素痣（nv）以及血管病变（血管瘤、血管角化瘤、化脓性肉芽肿和血管性病变等）。