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Computer-aided diagnosis systems for classification of different type of skinlesions have been an active field of research in recent decades. It has beenshown that introducing lesions and their attributes masks into lesionclassification pipeline can greatly improve the performance. In this paper, wepropose a framework by incorporating transfer learning for segmenting lesionsand their attributes based on the convolutional neural networks. The proposedframework is based on the encoder-decoder architecture which utilizes a varietyof pre-trained networks in the encoding path and generates the prediction mapby combining multi-scale information in decoding path using a pyramid poolingmanner. To address the lack of training data and increase the proposed modelgeneralization, an extensive set of novel domain-specific augmentation routineshave been applied to simulate the real variations in dermoscopy images.Finally, by performing broad experiments on three different data sets obtainedfrom International Skin Imaging Collaboration archive (ISIC2016, ISIC2017, andISIC2018 challenges data sets), we show that the proposed method outperformsother state-of-the-art approaches for ISIC2016 and ISIC2017 segmentation taskand achieved the first rank on the leader-board of ISIC2018 attribute detectiontask.

Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations