AMCC-Net: An asymmetric multi-cross convolution for skin lesion segmentation on dermoscopic images

Dayananda, Chaitra; Yamanakkanavar, Nagaraj; Nguyen, Truong Thao; Lee, Bumshik

doi:10.1016/j.engappai.2023.106154

Cited by 8 publications

(4 citation statements)

References 43 publications

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“…With this design, the network can better distinguish features in different directions within the image and more effectively capture directional information. Such asymmetric design enables the network to achieve better performance in image recognition tasks and provides new solutions for image segmentation tasks [36][37][38].…”

Section: Acnetmentioning

confidence: 99%

“…Let x i denote the input sample, where i is the index of the convolutional layer. Then, the output after the convolution of the first layer is calculated similarly to [36], as follows:…”

Section: Residual Double Asymmetric Convolution (Resdac) Blockmentioning

confidence: 99%

“…For the low-level feature maps, a 1 × 1 2D convolution is applied to introduce non-linearity and expand the model's options. This is why a "deep" network is preferable to a "wide" network [36], as the non-linear layer expands the model's capacity. The two processed feature maps are then fused through element-wise multiplication, and an SE block is employed for implementing an attention mechanism as to explicitly model the interdependence between channels and adaptively recalibrate the channelwise characteristics, thus enhancing the representation capability of the convolutional block for fused information.…”

Section: Adjacent Layer Feature Fusion (Alf) Blockmentioning

confidence: 99%

“…By copying the feature maps from the encoder, the model can capture multi-scale information. The skip connections allow utilization of both high-resolution and lowresolution information from the feature maps during the up-sampling process [36], as follows:…”

Section: Resdac-netmentioning

confidence: 99%

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ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels

Ji,

Liu,

et al. 2024

Med Biol Eng Comput

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The pancreas not only is situated in a complex abdominal background but is also surrounded by other abdominal organs and adipose tissue, resulting in blurred organ boundaries. Accurate segmentation of pancreatic tissue is crucial for computer-aided diagnosis systems, as it can be used for surgical planning, navigation, and assessment of organs. In the light of this, the current paper proposes a novel Residual Double Asymmetric Convolution Network (ResDAC-Net) model. Firstly, newly designed ResDAC blocks are used to highlight pancreatic features. Secondly, the feature fusion between adjacent encoding layers fully utilizes the low-level and deep-level features extracted by the ResDAC blocks. Finally, parallel dilated convolutions are employed to increase the receptive field to capture multiscale spatial information. ResDAC-Net is highly compatible to the existing state-of-the-art models, according to three (out of four) evaluation metrics, including the two main ones used for segmentation performance evaluation (i.e., DSC and Jaccard index). Graphical abstract

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Section: Acnetmentioning

confidence: 99%

“…Let x i denote the input sample, where i is the index of the convolutional layer. Then, the output after the convolution of the first layer is calculated similarly to [36], as follows:…”

Section: Residual Double Asymmetric Convolution (Resdac) Blockmentioning

confidence: 99%

Section: Adjacent Layer Feature Fusion (Alf) Blockmentioning

confidence: 99%

Section: Resdac-netmentioning

confidence: 99%

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