Automated Skin Lesion Segmentation Via an Adaptive Dual Attention Module

Wu, Huisi; Pan, Junquan; Li, Zhuoying; Wen, Zhenkun; Qin, Jing

doi:10.1109/tmi.2020.3027341

Cited by 122 publications

(47 citation statements)

References 30 publications

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“…It can be seen that there have been better performances for different models compared with ISIC 2018, and our weakly supervised segmentation method also achieved a competitive performance with other fully supervised segmentation models. From these related works, performed for skin lesion segmentation, we can see that our method achieves a competitive performance compared with the classic lesion segmenting methods-Wu's method [84], HRFB [88] and Att-Deeplab V3+ [79]. In addition, our method is different to these related methods.…”

Section: Isicmentioning

confidence: 85%

“…In addition, our method is different to these related methods. Specifically: (1) The existing methods for skin lesion segmentation are the methods based on the fully supervised learning, while our proposed method can carry out lesion segmentation based on weekly supervised learning using box level annotations; (2) ADAM [84] attention module, which includes Global Average Pooling (GAP) and Pixel Level Correlation (PC), is designed in Wu's method to capture global contextual information. HRFB [88] provides high-resolution feature mapping to preserve spatial details.…”

Section: Isicmentioning

confidence: 99%

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Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism

Xie

Zhang

Jiang³

et al. 2021

Electronics

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Computational intelligence has been widely used in medical information processing. The deep learning methods, especially, have many successful applications in medical image analysis. In this paper, we proposed an end-to-end medical lesion segmentation framework based on convolutional neural networks with a dual attention mechanism, which integrates both fully and weakly supervised segmentation. The weakly supervised segmentation module achieves accurate lesion segmentation by using bounding-box labels of lesion areas, which solves the problem of the high cost of pixel-level labels with lesions in the medical images. In addition, a dual attention mechanism is introduced to enhance the network’s ability for visual feature learning. The dual attention mechanism (channel and spatial attention) can help the network pay attention to feature extraction from important regions. Compared with the current mainstream method of weakly supervised segmentation using pseudo labels, it can greatly reduce the gaps between ground-truth labels and pseudo labels. The final experimental results show that our proposed framework achieved more competitive performances on oral lesion dataset, and our framework further extended to dermatological lesion segmentation.

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Section: Isicmentioning

confidence: 85%

Section: Isicmentioning

confidence: 99%

Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism

Xie

Zhang

Jiang³

et al. 2021

Electronics

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“…Tang et al [ 31 ] proposed to use context information to guide the feature coding process, and adopted a new deep monitoring objective function to supervise the entire network end-to-end. Wu et al [ 32 ] proposed an efficient and adaptive dual-attention module. Meanwhile, the backbone network adopts a dual-coding structure, which reduces redundancy and expands the network’s reception domain.…”

Section: Related Workmentioning

confidence: 99%

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

Dong

Wang

Cheng

et al. 2021

Sensors

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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.

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“…Huisi Wu et al. [ 42 ] proposed a deep learning model equipped with a new and efficient adaptive dual attention module (ADAM) to automatically segment skin lesions from dermoscopic images. Most of the above methods only focus on feature information from the two parts of spatial and channel.…”

Section: Work Organizationmentioning

confidence: 99%

Attention-Guided Network with Densely Connected Convolution for Skin Lesion Segmentation

Tao

Jiang

Cao

et al. 2021

Sensors

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The automatic segmentation of skin lesions is considered to be a key step in the diagnosis and treatment of skin lesions, which is essential to improve the survival rate of patients. However, due to the low contrast, the texture and boundary are difficult to distinguish, which makes the accurate segmentation of skin lesions challenging. To cope with these challenges, this paper proposes an attention-guided network with densely connected convolution for skin lesion segmentation, called CSAG and DCCNet. In the last step of the encoding path, the model uses densely connected convolution to replace the ordinary convolutional layer. A novel attention-oriented filter module called Channel Spatial Fast Attention-guided Filter (CSFAG for short) was designed and embedded in the skip connection of the CSAG and DCCNet. On the ISIC-2017 data set, a large number of ablation experiments have verified the superiority and robustness of the CSFAG module and Densely Connected Convolution. The segmentation performance of CSAG and DCCNet is compared with other latest algorithms, and very competitive results have been achieved in all indicators. The robustness and cross-data set performance of our method was tested on another publicly available data set PH2, further verifying the effectiveness of the model.

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Automated Skin Lesion Segmentation Via an Adaptive Dual Attention Module

Cited by 122 publications

References 30 publications

Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism

Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism

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

Attention-Guided Network with Densely Connected Convolution for Skin Lesion Segmentation

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