AS-Net: Attention Synergy Network for skin lesion segmentation

Hu, Kai; Lü, Jing; Lee, Dongjin; Xiong, Daxi; Chen, Zhineng

doi:10.1016/j.eswa.2022.117112

Cited by 37 publications

(12 citation statements)

References 44 publications

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“…e way to deal with this situation is to weigh FN more than FP. e Tversky similarity index is a generalization of the Dice coefficient that provides flexibility to its problems in balancing FPs and FNs as shown in equation (6).…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…erefore, deep convolutional neural networks (DCNN), which will automatically identify images, have been used to overcome this di cult task. Such systems try to determine lesion boundaries and make high-accuracy decisions based on skin lesion segmentation [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Czajkowska et al [14] proposed a model consisting of two DeepLab v3+ models with a ResNet-50 backbone and a fuzzy connectivity analysis module for fine segmentation. Hu et al [6] proposed a new attention synergy network (AS-Net) to improve the discriminative ability for skin lesion segmentation by combining spatial and channel attention mechanisms. Arora et al [15] proposed a modified U-Net-based segmentation model for automatic skin lesion segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Skin Lesion Segmentation Based on Edge Attention Vnet with Balanced Focal Tversky Loss

Ocal

Alhudhaif

Polat

2022

Mathematical Problems in Engineering

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Segmentation of skin lesions from dermoscopic images plays an essential role in the early detection of skin cancer. However, skin lesion segmentation is still challenging due to artifacts such as indistinguishability between skin lesion and normal skin, hair on the skin, and reflections in the obtained dermoscopy images. In this study, an edge attention network (ET-Net) combining edge guidance module (EGM) and weighted aggregation module is added to the 2D volumetric convolutional neural network (Vnet 2D) to maximize the performance of skin lesion segmentation. In addition, the proposed fusion model presents a new fusion loss function by combining balanced binary cross-entropy (BBCE) and focal Tversky loss (FTL). The proposed model has been tested on the ISIC 2018 Task 1 Lesion Boundary Segmentation Challenge dataset. The proposed model outperformed the state-of-the-art studies as a result of the tests.

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Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Skin Lesion Segmentation Based on Edge Attention Vnet with Balanced Focal Tversky Loss

Ocal

Alhudhaif

Polat

2022

Mathematical Problems in Engineering

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“…In W-Net [32], two concatenated encoder-decoder architectures are used, and different encoding blocks are used to encode the decoded features to form a 'W'-shaped model, which effectively improves the accuracy of segmentation of skin lesions. Hu et al [33] proposed Attention Synergy Network (AS-Net) for skin lesion segmentation, in which spatial and channel attention were combined for feature learning of skin lesions, and a weighted loss function was designed to emphasize skin lesion regions. Liu et al [34] proposed a neighbourhood contextual refinement network (NCRNet) for skin lesion segmentation, in which two separate but closely correlated decoders were used to position the skin lesion and refine the skin lesion boundary, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Hu et al. [33] proposed Attention Synergy Network (AS‐Net) for skin lesion segmentation, in which spatial and channel attention were combined for feature learning of skin lesions, and a weighted loss function was designed to emphasize skin lesion regions. Liu et al.…”

Section: Introductionmentioning

confidence: 99%

MDSC‐Net: A multi‐scale depthwise separable convolutional neural network for skin lesion segmentation

Jiang

Qiao

Zhang

et al. 2023

IET Image Processing

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Accurate segmentation of the skin lesion region is crucial for diagnosing and screening skin diseases. However, skin lesion segmentation is challenging due to the indistinguishable boundaries of the lesion region, irregular shapes and hair interference. To settle the above issues, we propose a Multi‐scale Depthwise Separable Convolutional Neural Network for skin lesion segmentation named MDSC‐Net. Specifically, a novel Multi‐scale Depthwise Separable Residual Convolution Module is employed in skip connection, conveying more detailed features to the decoder. To compensate for the loss of spatial location information in down‐sampling, we propose a novel Spatial Adaption Module. Furthermore, we propose a Multi‐scale Decoding Fusion Module in the decoder to capture contextual information. We have performed extensive experiments to verify the effectiveness and robustness of the proposed network on three public benchmark skin lesion segmentation datasets and one public benchmark polyp segmentation dataset, including ISIC‐2017, ISIC‐2018, PH2, and Kvasir‐SEG datasets. Experimental results consistently demonstrate the proposed MDSC‐Net achieves superior segmentation across five popularly used evaluation criteria. The proposed network reaches high‐performance skin lesion segmentation, and can provide important clues to help doctors diagnose and treat skin cancer early.

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Dermatologist-Level Classification of Skin Cancer with Level Set Method and Isolation Forest

Bellaj¹,

Boujena²,

Benmir³

2023

Lecture Notes in Electrical Engineering

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AS-Net: Attention Synergy Network for skin lesion segmentation

Cited by 37 publications

References 44 publications

Skin Lesion Segmentation Based on Edge Attention Vnet with Balanced Focal Tversky Loss

Skin Lesion Segmentation Based on Edge Attention Vnet with Balanced Focal Tversky Loss

MDSC‐Net: A multi‐scale depthwise separable convolutional neural network for skin lesion segmentation

Dermatologist-Level Classification of Skin Cancer with Level Set Method and Isolation Forest

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