  Early detection and segmentation of skin lesions is crucial for timely
diagnosis and treatment, necessary to improve the survival rate of patients.
However, manual delineation is time consuming and subject to intra- and
inter-observer variations among dermatologists. This underlines the need for an
accurate and automatic approach to skin lesion segmentation. To tackle this
issue, we propose a multi-task convolutional neural network (CNN) based, joint
detection and segmentation framework, designed to initially localize the lesion
and subsequently, segment it. A `Faster region-based convolutional neural
network' (Faster-RCNN) which comprises a region proposal network (RPN), is used
to generate bounding boxes/region proposals, for lesion localization in each
image. The proposed regions are subsequently refined using a softmax classifier
and a bounding-box regressor. The refined bounding boxes are finally cropped
and segmented using `SkinNet', a modified version of U-Net. We trained and
evaluated the performance of our network, using the ISBI 2017 challenge and the
PH2 datasets, and compared it with the state-of-the-art, using the official
test data released as part of the challenge for the former. Our approach
outperformed others in terms of Dice coefficients ($>0.93$), Jaccard index
($>0.88$), accuracy ($>0.96$) and sensitivity ($>0.95$), across five-fold cross
validation experiments.
