Step-wise integration of deep class-specific learning for dermoscopic image segmentation

Bi, Lei, Kim, Jinman, Ahn, Euijoon, Kumar, Ashnil, Feng, Dagan, and Fulham, Michael (2019) Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern Recognition, 85. pp. 78-89.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: https://doi.org/10.1016/j.patcog.2018.08...


Abstract

The segmentation of abnormal regions on dermoscopic images is an important step for automated computer aided diagnosis (CAD) of skin lesions. Recent methods based on fully convolutional networks (FCN) have been very successful for dermoscopic image segmentation. However, they tend to overfit to the visual characteristics that are present in the dominant non-melanoma studies and therefore, perform poorly on the complex visual characteristics exhibited by melanoma studies, which usually consists of fuzzy boundaries and heterogeneous textures. In this paper, we propose a new method for automated skin lesion segmentation that overcomes these limitations via a novel deep class-specific learning approach which learns the important visual characteristics of the skin lesions of each individual class (melanoma vs. non-melanoma) on an individual basis. We also introduce a new probability-based, step-wise integration to combine complementary segmentation results derived from individual class-specific learning models. We achieved an average Dice coefficient of 85.66% on the ISBI 2017 Skin Lesion Challenge (SLC), 91.77% on the ISBI 2016 SLC and 92.10% on the PH2 datasets with corresponding Jaccard indices of 77.73%, 85.92% and 85.90%, respectively, for the same datasets. Our experiments on three well-established public benchmark datasets demonstrate that our method is more effective than other state-of-the-art methods for skin lesion segmentation.

Item ID: 72033
Item Type: Article (Research - C1)
ISSN: 1873-5142
Copyright Information: © 2018 Elsevier Ltd. All rights reserved.
Funders: Australian Research Council (ARC)
Projects and Grants: ARC LP140100686
Date Deposited: 23 Mar 2022 23:40
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460308 Pattern recognition @ 40%
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing @ 60%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 60%
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220404 Computer systems @ 40%
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page