  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.
