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Automatic Skin Lesion Classification Based on Mid-level Feature Learning, Computerized Medical Imaging and Graphics

Liu, Lina and Mou, LiChao and Zhu, Xiao Xiang and Mandal, Mrinal (2020) Automatic Skin Lesion Classification Based on Mid-level Feature Learning, Computerized Medical Imaging and Graphics. Computerized Medical Imaging and Graphics, 84, p. 101765. Elsevier. doi: 10.1016/j.compmedimag.2020.101765. ISSN 0895-6111.

Full text not available from this repository.

Official URL: https://www.sciencedirect.com/science/article/abs/pii/S0895611120300689

Abstract

Dermoscopic images are widely used for melanoma detection. Many existing works based on traditional classification methods and deep learning models have been proposed for automatic skin lesion analysis. The traditional classification methods use hand-crafted features as input. However, due to the strong visual similarity between different classes of skin lesions and complex skin conditions, the hand-crafted features are not discriminative enough and fail in many cases. Recently, deep convolutional neural networks (CNN) have gained popularity since they can automatically learn optimal features during the training phase. Different from existing works, a novel mid-level feature learning method for skin lesion classification task is proposed in this paper. In this method, skin lesion segmentation is first performed to detect the regions of interest (ROI) of skin lesion images. Next, pretrained neural networks including ResNet and DenseNet are used as the feature extractors for the ROI images. Instead of using the extracted features directly as input of classifiers, the proposed method obtains the mid-level feature representations by utilizing the relationships among different image samples based on distance metric learning. The learned feature representation is a soft discriminative descriptor, having more tolerance to the hard samples and hence is more robust to the large intra-class difference and inter-class similarity. Experimental results demonstrate advantages of the proposed mid-level features, and the proposed method obtains state-of-the-art performance compared with the existing CNN based methods.

Item URL in elib:https://elib.dlr.de/140925/
Document Type:Article
Title:Automatic Skin Lesion Classification Based on Mid-level Feature Learning, Computerized Medical Imaging and Graphics
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Liu, LinaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Mou, LiChaoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Zhu, Xiao XiangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Mandal, MrinalUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:September 2020
Journal or Publication Title:Computerized Medical Imaging and Graphics
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:84
DOI:10.1016/j.compmedimag.2020.101765
Page Range:p. 101765
Publisher:Elsevier
ISSN:0895-6111
Status:Published
Keywords:Skin lesion analysis, Medical image analysis, Feature learning, Metric learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Bratasanu, Ion-Dragos
Deposited On:12 Feb 2021 18:10
Last Modified:12 Feb 2021 18:10

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