  With a large influx of dermoscopy images and a growing shortage of
dermatologists, automatic dermoscopic image analysis plays an essential role in
skin cancer diagnosis. In this paper, a new deep fully convolutional neural
network (FCNN) is proposed to automatically segment melanoma out of skin images
by end-to-end learning with only pixels and labels as inputs. Our proposed FCNN
is capable of using both local and global information to segment melanoma by
adopting skipping layers. The public benchmark database consisting of 150
validation images, 600 test images and 2000 training images in the melanoma
detection challenge 2017 at International Symposium Biomedical Imaging 2017 is
used to test the performance of our algorithm. All large size images (for
example, $4000\times 6000$ pixels) are reduced to much smaller images with
$384\times 384$ pixels (more than 10 times smaller). We got and submitted
preliminary results to the challenge without any pre or post processing. The
performance of our proposed method could be further improved by data
augmentation and by avoiding image size reduction.
