Leveraging Adaptive Color Augmentation in Convolutional Neural Networks for Deep Skin Lesion Segmentation

Saha, Anindo; Prasad, P.W.C.; Thabit, Abdullah

doi:10.1109/isbi45749.2020.9098344

Cited by 13 publications

(4 citation statements)

References 17 publications

(23 reference statements)

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“…Lu et al [40] implemented the Shrinkage loss function to balance the number of data of the different classes. Saha et al [41] designed a color enhancement strategy to enrich and expand the dataset by decomposing the image into layers of different hues. Li et al [42] used a deep learning method as a preprocessing method for hair removal, which has a significant improvement in enhancing the segmentation of the model.…”

Section: Loss Functions and Preprocessing Methodsmentioning

confidence: 99%

Dermoscopic image segmentation based on Pyramid Residual Attention Module

Jiang¹,

Cheng²,

Dong³

et al. 2022

PLoS ONE

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We propose a stacked convolutional neural network incorporating a novel and efficient pyramid residual attention (PRA) module for the task of automatic segmentation of dermoscopic images. Precise segmentation is a significant and challenging step for computer-aided diagnosis technology in skin lesion diagnosis and treatment. The proposed PRA has the following characteristics: First, we concentrate on three widely used modules in the PRA. The purpose of the pyramid structure is to extract the feature information of the lesion area at different scales, the residual means is aimed to ensure the efficiency of model training, and the attention mechanism is used to screen effective features maps. Thanks to the PRA, our network can still obtain precise boundary information that distinguishes healthy skin from diseased areas for the blurred lesion areas. Secondly, the proposed PRA can increase the segmentation ability of a single module for lesion regions through efficient stacking. The third, we incorporate the idea of encoder-decoder into the architecture of the overall network. Compared with the traditional networks, we divide the segmentation procedure into three levels and construct the pyramid residual attention network (PRAN). The shallow layer mainly processes spatial information, the middle layer refines both spatial and semantic information, and the deep layer intensively learns semantic information. The basic module of PRAN is PRA, which is enough to ensure the efficiency of the three-layer architecture network. We extensively evaluate our method on ISIC2017 and ISIC2018 datasets. The experimental results demonstrate that PRAN can obtain better segmentation performance comparable to state-of-the-art deep learning models under the same experiment environment conditions.

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Section: Loss Functions and Preprocessing Methodsmentioning

confidence: 99%

Dermoscopic image segmentation based on Pyramid Residual Attention Module

Jiang¹,

Cheng²,

Dong³

et al. 2022

PLoS ONE

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show abstract

“…Table 5 shows the comparative analysis with state-of-art algorithms. [40] 0.844 0.749 0.910 0.925 MFSNet [41] 0.988 0.975 0.99 0.999 Al et al [29] 0.855 0.773 0.825 0.980 Proposed 0.991 0.981 0.991 0.999 HAM10000 UNet [16] 0.781 0.774 0.799 0.802 Double UNet [34] 0.843 0.812 0.961 0.845 SegNet [34] 0.816 0.821 0.867 0.854 MFSNet [34] 0.906 0.902 0.999 0.99 Saha et al [42] 0.891 0.819 0.824 0.981 Abraham et al [44] 0.856 ---Shahin et al [45] 0903 0.837 0.902 0.974 Bissoto et al [46] 0.873 0.792 0.934 0.936 Ibtehaz et al [47] -0.803 --Proposed 0.951 0.943 0.9510 0.9810…”

Section: Evaluation Metricsmentioning

confidence: 99%

Hybrid channel and spatial attention-UNet for skin lesion segmentation

Gadag,

Palraj

2024

IJ-AI

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<span>Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.</span>

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“…Hafhouf et al [ 29 ] combined the extended convolution and pyramid pooling module and used it in the codec structure to improve the segmentation result. Saha et al [ 30 ] proposed a color enhancement technique that adaptively enhances the data and distinguishes the structural features of normal skin from damaged skin tissue through deep visualization. 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.…”

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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Leveraging Adaptive Color Augmentation in Convolutional Neural Networks for Deep Skin Lesion Segmentation

Cited by 13 publications

References 17 publications

Dermoscopic image segmentation based on Pyramid Residual Attention Module

Dermoscopic image segmentation based on Pyramid Residual Attention Module

Hybrid channel and spatial attention-UNet for skin lesion segmentation

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

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