AFLN-DGCL: Adaptive Feature Learning Network With Difficulty-Guided Curriculum Learning for skin lesion segmentation

2021 ◽  
pp. 107656
Author(s):  
Peng Tang ◽  
Xintong Yan ◽  
Qiaokang Liang ◽  
Dan Zhang
Keyword(s):  
Skin Lesion ◽  
Feature Learning ◽  
Lesion Segmentation ◽  
Learning Network

scholarly journals FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5172
Author(s):  
Yuying Dong ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Yongming Li
Keyword(s):  
Skin Lesion ◽  
Real Life ◽  
Good Deal ◽  
Experimental Tests ◽  
Skin Lesions ◽  
Lesion Segmentation ◽  
Attention Network ◽  
Effective Feedback ◽  
Public Datasets ◽  
Learning Architectures

Considerable research and surveys indicate that skin lesions are an early symptom of skin cancer. Segmentation of skin lesions is still a hot research topic. Dermatological datasets in skin lesion segmentation tasks generated a large number of parameters when data augmented, limiting the application of smart assisted medicine in real life. Hence, this paper proposes an effective feedback attention network (FAC-Net). The network is equipped with the feedback fusion block (FFB) and the attention mechanism block (AMB), through the combination of these two modules, we can obtain richer and more specific feature mapping without data enhancement. Numerous experimental tests were given by us on public datasets (ISIC2018, ISBI2017, ISBI2016), and a good deal of metrics like the Jaccard index (JA) and Dice coefficient (DC) were used to evaluate the results of segmentation. On the ISIC2018 dataset, we obtained results for DC equal to 91.19% and JA equal to 83.99%, compared with the based network. The results of these two main metrics were improved by more than 1%. In addition, the metrics were also improved in the other two datasets. It can be demonstrated through experiments that without any enhancements of the datasets, our lightweight model can achieve better segmentation performance than most deep learning architectures.

Skin lesion segmentation from dermoscopic images by using Mask R-CNN, Retina-Deeplab, and graph-based methods

2021 ◽  
Vol 67 ◽  
pp. 102533
Author(s):  
Fatemeh Bagheri ◽  
Mohammad Jafar Tarokh ◽  
Majid Ziaratban
Keyword(s):  
Skin Lesion ◽  
Lesion Segmentation

scholarly journals Multiview Deep Feature Learning Network for SAR Automatic Target Recognition

2021 ◽  
Vol 13 (8) ◽  
pp. 1455
Author(s):  
Jifang Pei ◽  
Weibo Huo ◽  
Chenwei Wang ◽  
Yulin Huang ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Target Recognition ◽  
Feature Learning ◽  
Automatic Target Recognition ◽  
Operating Conditions ◽  
Sar Images ◽  
Single View ◽  
Learning Network ◽  
Deep Feature ◽  
Deep Feature Learning ◽  
Classification Information

Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.

scholarly journals Lightweight encoder-decoder model for automatic skin lesion segmentation

2021 ◽  
pp. 100640
Author(s):  
Adi Wibowo ◽  
Satriawan Rasyid Purnama ◽  
Panji Wisnu Wirawan ◽  
Hanif Rasyidi
Keyword(s):  
Skin Lesion ◽  
Lesion Segmentation
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