SLT-Net: A codec network for skin lesion segmentation

Feng, Kaili; Ren, Lili; Wang, Guanglei; Wang, Hongrui; Li, Yan

doi:10.1016/j.compbiomed.2022.105942

Cited by 13 publications

(15 citation statements)

References 29 publications

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“…In our study, we trained and compared a total of 15 models on our lumbar spine MRI dataset. These models are Attention U-Net (Oktay et al, 2018), HSNet (Zhang et al, 2022), Inception-SwinUnet (Pu et al, 2023), MedT (Valanarasu et al, 2021), MultiResUNet (Ibtehaz and Rahman, 2020), SLT-Net (Feng et al, 2022), Swin-Unet (Cao et al, 2023), UNETR (Hatamizadeh et al, 2021), Swin UNETR (Hatamizadeh et al, 2022), TransUNet (Chen et al, 2021), UCTransNet (Wang et al, 2022), UNet++ (Zhou et al, 2018), UNeXt (Valanarasu and Patel, 2022), UTNet (Gao et al, 2021), and BianqueNet (Zheng et al, 2022). We were unable to test all models mentioned in Section 2 due to either unavailability (e.g., no source code) or incompatibility (e.g., size not matching).…”

Section: Methodsmentioning

confidence: 99%

“…BianqueNet utilized the position embedding within Swin-Transformer (Zheng et al, 2022). According to the source code, SLT-Net introduces a lepe distance to represent the position bias (embedding), which is directly added into the output matrix (Feng et al, 2022). MedT proposed a gated position-sensitive axial attention mechanism where four learnable gates ( G ) control the amount of position embedding contained in key ( K ), query ( Q ) and value ( V ) embeddings (Valanarasu et al, 2021).…”

Section: Review Of Dnn Models For Lumbar Image Segmentationmentioning

confidence: 99%

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Evaluation of Deep Neural Network Models for Instance Segmentation of Lumbar Spine MRI

Chen,

Qian,

et al. 2024

Preprint

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Intervertebral disc disease, a prevalent ailment, frequently leads to intermittent or persistent low back pain, and diagnosing and assessing of this disease rely on accurate measurement of vertebral bone and intervertebral disc geometries from lumbar MR images. Deep neural network (DNN) models may assist clinicians with more efficient image segmentation of individual instances (discs and vertebrae) of the lumbar spine in an automated way, which is termed as instance image segmentation. In this work, we evaluated 15 existing DNN models for lumbar spine MR image segmentation. We introduced a new data augmentation technique to create synthetic yet realistic MR image dataset, named SSMSpine, which is made publicly available. The 15 image segmentation models are evaluated on our private in-house dataset and the public SSMSpine dataset, using two metrics, Dice Similarity Coefficient and 95% Hausdorff Distance. The SSMSpine dataset are available at https://github.com/jiasongchen/SSMSpine.

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Section: Methodsmentioning

confidence: 99%

Section: Review Of Dnn Models For Lumbar Image Segmentationmentioning

confidence: 99%

Evaluation of Deep Neural Network Models for Instance Segmentation of Lumbar Spine MRI

Chen,

Qian,

et al. 2024

Preprint

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“…Feng et al. [20] proposed skin lesion transformer (SLT‐Net) to further lighten impacts of erroneous segmentation. In this method UNet network is further enhanced from prospective of global and local information extraction and multi‐scale contextual information.…”

Section: Related Workmentioning

confidence: 99%

“…Convolutional neural network (CNN) have made numerous achievements in the field of medical image segmentation, especially since the emergence of UNet [13,14], which has brought a new era of development in medical image segmentation. UNet framework and its several variants have drawn a lot of attention since such methods can collect local and global context data and owing to robustness, efficiency, interpretability, reliable computational cost, among other deep learning methods [14][15][16][17][18][19][20][21]. In comparison to traditional methods, the use of CNNs to support image segmentation in clinical perspectives has gained a lot of attention.…”

Section: Introductionmentioning

confidence: 99%

“…The method is described in Sections 4 and 5; Experiments and results are discussed in Section 6; the method's limitations are highlighted in Section 7; and our findings are summarized in Section 8. Feng et al [20] proposed skin lesion transformer (SLT-Net) to further lighten impacts of erroneous segmentation. In this method UNet network is further enhanced from prospective of global and local information extraction and multi-scale contextual information.…”

Section: Introductionmentioning

confidence: 99%

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MSPAN: Multi‐scale pyramid attention network for efficient skin cancer lesion segmentation

Ahmed,

Xin,

Lizhuang

2024

IET Image Processing

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Skin cancer is common and deadly, needs to be detected and treated properly. Deep learning algorithms like UNet have shown potential results in medical imaging. Such approaches still struggle to capture fine‐grained details and scale differences in skin lesions‐based occlusions' appearance, size etc. This research proposes a redesign UNet, the Multi‐Scale Pyramid Attention Network (MSPAN), to improve skin cancer lesion segmentation. The input data is processed at numerous scales with varied receptive fields. This enhances the network's ability to identify lesion locations by capturing local and global context. Attention approaches also help the network to suppress noise by focusing on informative features. We have evaluated MSPAN model on the publicly available ISIC2018 benchmark dataset for skin lesion segmentation. The method surpasses traditional UNet and other current methods in accuracy and effectiveness. The model also has a post‐processing to estimate lesion area for fast inference, making it suitable for extensive screening. Redesigned UNet with the Multi‐Scale Pyramid Attention Network improves skin cancer lesion segmentation. The model's ability to collect fine‐grained information and handle occlusions allows for more accurate skin cancer diagnosis and treatment. The MSPAN design can improve computer‐aided diagnosis systems and help dermatologists make precise clinical decisions.

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Masked autoencoders with generalizable self-distillation for skin lesion segmentation

Zhi,

Bie,

Wang

et al. 2024

Med Biol Eng Comput

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SLT-Net: A codec network for skin lesion segmentation

Cited by 13 publications

References 29 publications

Evaluation of Deep Neural Network Models for Instance Segmentation of Lumbar Spine MRI

Evaluation of Deep Neural Network Models for Instance Segmentation of Lumbar Spine MRI

MSPAN: Multi‐scale pyramid attention network for efficient skin cancer lesion segmentation

Masked autoencoders with generalizable self-distillation for skin lesion segmentation

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