DCACNet: Dual context aggregation and attention-guided cross deconvolution network for medical image segmentation

Lu, Hongchun; Tian, Shengwei; Yu, Long; Liu, Lu; Cheng, Junlong; Wu, Weidong; Kang, Xiaojing; Zhang, Dezhi

doi:10.1016/j.cmpb.2021.106566

Cited by 12 publications

(7 citation statements)

References 78 publications

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“…Moreover, with the passage of time, the accuracy of these methods has also been considerably improved in recognition and prediction. The DeconvNet [21] is a more extensive decoder than the original FCN. The mentioned decoder is balanced in number and feature size with the encoder.…”

Section: Literature Reviewmentioning

confidence: 99%

A Semantic Adversarial Network for Detection and Classification of Myopic燤aculopathy

Abbas¹,

Baig²,

Hussain³

2023

Computers, Materials &Amp; Continua

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The diagnosis of eye disease through deep learning (DL) technology is the latest trend in the field of artificial intelligence (AI). Especially in diagnosing pathologic myopia (PM) lesions, the implementation of DL is a difficult task because of the classification complexity and definition system of PM. However, it is possible to design an AI-based technique that can identify PM automatically and help doctors make relevant decisions. To achieve this objective, it is important to have adequate resources such as a high-quality PM image dataset and an expert team. The primary aim of this research is to design and train the DLs to automatically identify and classify PM into different classes. In this article, we have developed a new class of DL models (SAN-FSL) for the segmentation and detection of PM through semantic adversarial networks (SAN) and few-short learning (FSL) methods, respectively. Compared to DL methods, the conventional segmentation methods use supervised learning models, so they (a) require a lot of data for training and (b) fixed weights are used after the completion of the training process. To solve such problems, the FSL technique was employed for model training with few samples. The ability of FSL learning in UNet architectures is being explored, and to fine-tune the weights, a few new samples are being provided to the UNet. The outcomes show improvement in the detection area and classification of PM stages. Betterment in the result is observed by sensitivity (SE) of 95%, specificity (SP) of 96%, and area under the receiver operating curve (AUC) of 98%, and the higher F1-score is achieved using 10-fold crossvalidation. Furthermore, the obtained results confirmed the superiority of the SAN-FSL method.

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Section: Literature Reviewmentioning

confidence: 99%

A Semantic Adversarial Network for Detection and Classification of Myopic燤aculopathy

Abbas¹,

Baig²,

Hussain³

2023

Computers, Materials &Amp; Continua

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“…The dimension of the parameter is 𝜔 ∈ R C×K×1×1 , and we abbreviate it as 𝜔 ∈ R C×K . Each channel in P represents the segmentation result of a class, as shown in Figure 2, which can be formulated as Equation (1).…”

Section: Kcrmmentioning

confidence: 99%

“…For example, automatically segmenting stomach and intestines in MRI scans can reduce the daily time cost of locating organs, which is helpful for adjusting the direction of the x-ray beams to increase the dose delivery to the tumor and avoid the stomach and intestines. Precise segmentation of abdominal organs, 1,2 facilitates pre-evaluation or detailed analysis of organs prior to transplant surgery. At the same time, automatic segmentation of skin lesion, [3][4][5][6] can help clinicians to judge the severity of the disease.…”

Section: Introductionmentioning

confidence: 99%

Class key feature extraction and fusion for 2D medical image segmentation

et al. 2023

Self Cite

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BackgroundThe size variation, complex semantic environment and high similarity in medical images often prevent deep learning models from achieving good performance.PurposeTo overcome these problems and improve the model segmentation performance and generalizability.MethodsWe propose the key class feature reconstruction module (KCRM), which ranks channel weights and selects key features (KFs) that contribute more to the segmentation results for each class. Meanwhile, KCRM reconstructs all local features to establish the dependence relationship from local features to KFs. In addition, we propose the spatial gating module (SGM), which employs KFs to generate two spatial maps to suppress irrelevant regions, strengthening the ability to locate semantic objects. Finally, we enable the model to adapt to size variations by diversifying the receptive field.ResultsWe integrate these modules into class key feature extraction and fusion network (CKFFNet) and validate its performance on three public medical datasets: CHAOS, UW‐Madison, and ISIC2017. The experimental results show that our method achieves better segmentation results and generalizability than those of mainstream methods.ConclusionThrough quantitative and qualitative research, the proposed module improves the segmentation results and enhances the model generalizability, making it suitable for application and expansion.

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“…The DAR-Net proposed by Li and Wang [ 31 ] has used a dense residual module, and it added the channel and spatial attention to the module to select the feature information in the feature map that performed well on the multiple data sets. The DCACNet proposed by Lu et al [ 32 ] has also applied the dual attention mechanism to the medical image segmentation, thus establishing the pixel associations and adding feature representations.…”

Section: Related Workmentioning

confidence: 99%

“…e DCACNet proposed by Lu et al [32] has also applied the dual attention mechanism to the medical image segmentation, thus establishing the pixel associations and adding feature representations.…”

Section: Related Workmentioning

confidence: 99%

Generative Adversarial Network Combined with SE-ResNet and Dilated Inception Block for Segmenting Retinal Vessels

Chen

Wang

et al. 2022

Computational Intelligence and Neuroscience

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This study develops an accurate method based on the generative adversarial network (GAN) that targets the issue of the current discontinuity of micro vessel segmentation in the retinal segmentation images. The processing of images has become increasingly efficient since the advent of deep learning method. We have proposed an improved GAN combined with SE-ResNet and dilated inception block for the segmenting retinal vessels (SAD-GAN). The GAN model has been improved with respect to the following points. (1) In the generator, the original convolution block is replaced with SE-ResNet module. Furthermore, SE-Net can extract the global channel information, while concomitantly strengthening and weakening the key features and invalid features, respectively. The residual structure can alleviate the issue of gradient disappearance. (2) The inception block and dilated convolution are introduced into the discriminator, which enhance the transmission of features and expand the acceptance domain for improved extraction of the deep network features. (3) We have included the attention mechanism in the discriminator for combining the local features with the corresponding global dependencies, and for highlighting the interdependent channel mapping. SAD-GAN performs satisfactorily on public retina datasets. On DRIVE dataset, ROC_AUC and PR_AUC reach 0.9813 and 0.8928, respectively. On CHASE_DB1 dataset, ROC_AUC and PR_AUC reach 0.9839 and 0.9002, respectively. Experimental results demonstrate that the generative adversarial model, combined with deep convolutional neural network, enhances the segmentation accuracy of the retinal vessels far above that of certain state-of-the-art methods.

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DCACNet: Dual context aggregation and attention-guided cross deconvolution network for medical image segmentation

Cited by 12 publications

References 78 publications

A Semantic Adversarial Network for Detection and Classification of Myopic燤aculopathy

A Semantic Adversarial Network for Detection and Classification of Myopic燤aculopathy

Class key feature extraction and fusion for 2D medical image segmentation

Generative Adversarial Network Combined with SE-ResNet and Dilated Inception Block for Segmenting Retinal Vessels

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