Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-class Semantic Segmentation of Skin Lesions via Fully
  Convolutional Networks   Melanoma is clinically difficult to distinguish from common benign skin
lesions, particularly melanocytic naevus and seborrhoeic keratosis. The
dermoscopic appearance of these lesions has huge intra-class variations and
high inter-class visual similarities. Most current research is focusing on
single-class segmentation irrespective of classes of skin lesions. In this
work, we evaluate the performance of deep learning on multi-class segmentation
of ISIC-2017 challenge dataset, which consists of 2,750 dermoscopic images. We
propose an end-to-end solution using fully convolutional networks (FCNs) for
multi-class semantic segmentation to automatically segment the melanoma,
seborrhoeic keratosis and naevus. To improve the performance of FCNs, transfer
learning and a hybrid loss function are used. We evaluate the performance of
the deep learning segmentation methods for multi-class segmentation and lesion
diagnosis (with post-processing method) on the testing set of the ISIC-2017
challenge dataset. The results showed that the two-tier level transfer learning
FCN-8s achieved the overall best result with \textit{Dice} score of 78.5% in a
naevus category, 65.3% in melanoma, and 55.7% in seborrhoeic keratosis in
multi-class segmentation and Accuracy of 84.62% for recognition of melanoma in
lesion diagnosis.

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The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat