  Skin lesion segmentation is a crucial step in the computer-aided diagnosis of
dermoscopic images. In the last few years, deep learning based semantic
segmentation methods have significantly advanced the skin lesion segmentation
results. However, the current performance is still unsatisfactory due to some
challenging factors such as large variety of lesion scale and ambiguous
difference between lesion region and background. In this paper, we propose a
simple yet effective framework, named Dual Objective Networks (DONet), to
improve the skin lesion segmentation. Our DONet adopts two symmetric decoders
to produce different predictions for approaching different objectives.
Concretely, the two objectives are actually defined by different loss
functions. In this way, the two decoders are encouraged to produce
differentiated probability maps to match different optimization targets,
resulting in complementary predictions accordingly. The complementary
information learned by these two objectives are further aggregated together to
make the final prediction, by which the uncertainty existing in segmentation
maps can be significantly alleviated. Besides, to address the challenge of
large variety of lesion scales and shapes in dermoscopic images, we
additionally propose a recurrent context encoding module (RCEM) to model the
complex correlation among skin lesions, where the features with different scale
contexts are efficiently integrated to form a more robust representation.
Extensive experiments on two popular benchmarks well demonstrate the
effectiveness of the proposed DONet. In particular, our DONet achieves 0.881
and 0.931 dice score on ISIC 2018 and $\text{PH}^2$, respectively. Code will be
made public available.
