kin cancer is considered one of the most common type of cancer in several
countries. Due to the difficulty and subjectivity in the clinical diagnosis of
skin lesions, Computer-Aided Diagnosis systems are being developed for assist
experts to perform more reliable diagnosis. The clinical analysis and diagnosis
of skin lesions relies not only on the visual information but also on the
context information provided by the patient. This work addresses the problem of
pigmented skin lesions detection from smartphones captured images. In addition
to the features extracted from images, patient context information was
collected to provide a more accurate diagnosis. The experiments showed that the
combination of visual features with context information improved final results.
Experimental results are very promising and comparable to experts.

该研究开发了一个基于智能手机拍摄的脂肪瘤皮肤病变图像的计算机辅助诊断系统，通过提取图像特征和病人背景信息来提高皮肤病变诊断的准确性，在实验中与专家的判断相当。

基于临床图像特征与患者数据的色素性皮肤病自动诊断技术

Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data

Training of neural networks for automated diagnosis of pigmented skin lesions
is hampered by the small size and lack of diversity of available datasets 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. Given this diversity we had to apply different
acquisition and cleaning methods and developed semi-automatic workflows
utilizing specifically trained neural networks. The final dataset consists of
10015 dermatoscopic images which are released as a training set for academic
machine learning purposes and are publicly available through the ISIC archive.
This benchmark dataset can be used for machine learning and for comparisons
with human experts. Cases include a representative collection of all important
diagnostic categories in the realm of pigmented lesions. More than 50% of
lesions have been confirmed by pathology, while the ground truth for the rest
of the cases was either follow-up, expert consensus, or confirmation by in-vivo
confocal microscopy.

利用具有不同种族和不同获取模式的皮肤镜图像，通过不同的采集和清洗方法以及半自动工作流程和特别训练的神经网络，我们成立了 HAM10000 数据集，这个数据集由 10015 个用于学术机器学习目的的皮肤镜图像组成，覆盖养猪场多种重要诊断范畴，用于训练神经网络。