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: |
| ||||||||||||||||||||
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 |
Repository Staff Only: item control page