  Accurate segmentation of skin lesion from dermoscopic images is a crucial
part of computer-aided diagnosis of melanoma. It is challenging due to the fact
that dermoscopic images from different patients have non-negligible lesion
variation, which causes difficulties in anatomical structure learning and
consistent skin lesion delineation. In this paper, we propose a novel
bi-directional dermoscopic feature learning (biDFL) framework to model the
complex correlation between skin lesions and their informative context. By
controlling feature information passing through two complementary directions, a
substantially rich and discriminative feature representation is achieved.
Specifically, we place biDFL module on the top of a CNN network to enhance
high-level parsing performance. Furthermore, we propose a multi-scale
consistent decision fusion (mCDF) that is capable of selectively focusing on
the informative decisions generated from multiple classification layers. By
analysis of the consistency of the decision at each position, mCDF
automatically adjusts the reliability of decisions and thus allows a more
insightful skin lesion delineation. The comprehensive experimental results show
the effectiveness of the proposed method on skin lesion segmentation, achieving
state-of-the-art performance consistently on two publicly available dermoscopic
image databases.
