  Dermoscopy image detection stays a tough task due to the weak distinguishable
property of the object.Although the deep convolution neural network
signifigantly boosted the performance on prevelance computer vision tasks in
recent years,there remains a room to explore more robust and precise models to
the problem of low contrast image segmentation.Towards the challenge of Lesion
Segmentation in ISBI 2017,we built a symmetrical identity inception fully
convolution network which is based on only 10 reversible inception blocks,every
block composed of four convolution branches with combination of different layer
depth and kernel size to extract sundry semantic features.Then we proposed an
approximate loss function for jaccard index metrics to train our model.To
overcome the drawbacks of traditional convolution,we adopted the dilation
convolution and conditional random field method to rectify our segmentation.We
also introduced multiple ways to prevent the problem of overfitting.The
experimental results shows that our model achived jaccard index of 0.82 and
kept learning from epoch to epoch.
