In order to improve the detection and classification of malignant melanoma,
this paper describes an image-based method that can achieve AUROC values of up
to 0.78 without additional clinical information. Furthermore, the importance of
the domain gap between two different image sources is considered, as it is
important to create usability independent of hardware components such as the
high-resolution scanner used. Since for the application of machine learning
methods, alterations of scanner-specific properties such as brightness,
contrast or sharpness can have strong (negative) effects on the quality of the
prediction methods, two ways to overcome this domain gap are discussed in this
paper.

本文介绍了基于图像的方法用于检测和分类恶性黑色素瘤的方法，并讨论了两种消除来自不同图像来源的域差异的方法，以实现硬件无关性和预测质量的提高。

利用神经网络对恶性黑色素瘤进行风险分层

Risk stratification of malignant melanoma using neural networks

Malignant melanoma has one of the most rapidly increasing incidences in the
world and has a considerable mortality rate. Early diagnosis is particularly
important since melanoma can be cured with prompt excision. Dermoscopy images
play an important role in the non-invasive early detection of melanoma [1].
However, melanoma detection using human vision alone can be subjective,
inaccurate and poorly reproducible even among experienced dermatologists. This
is attributed to the challenges in interpreting images with diverse
characteristics including lesions of varying sizes and shapes, lesions that may
have fuzzy boundaries, different skin colors and the presence of hair [2].
Therefore, the automatic analysis of dermoscopy images is a valuable aid for
clinical decision making and for image-based diagnosis to identify diseases
such as melanoma [1-4]. Deep residual networks (ResNets) has achieved
state-of-the-art results in image classification and detection related problems
[5-8]. In this ISIC 2017 skin lesion analysis challenge [9], we propose to
exploit the deep ResNets for robust visual features learning and
representations.

这篇文章讨论了利用深度残差网络对皮肤镜图像进行分析和诊断的方法，通过自动分析可以更准确地进行皮肤黑素瘤的诊断，有助于提高早期诊断率和治疗效果。