Stroke Lesion Segmentation from Low-Quality and Few-Shot MRIs via Similarity-Weighted Self-ensembling Framework

Zhang, Dong; Confidence, Raymond; Anazodo, Udunna

doi:10.1007/978-3-031-16443-9_9

Cited by 7 publications

(9 citation statements)

References 20 publications

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“…In contrast, Zhang et al. [46] integrated a discern network block comprises of residual blocks with identify block composed of channel and spatial wise self attention mechanisms. However, proposed method used parallel integration of attention modules and then fused with MSPM module via utilizing global average pooling to leverage multi‐scaling process.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…For example, Guo et al [45] integrated spatial attention by applying maximum and average pooling operations along with channel axis and then concatenate them to produce an efficient feature descriptor. In contrast, Zhang et al [46] integrated a discern network block comprises of residual blocks with identify block composed of channel and spatial wise self attention mechanisms. However, proposed method used parallel integration of attention modules and then fused with MSPM module via utilizing global average pooling to leverage multiscaling process.…”

Section: Efficient Channel and Spatial Attention Modulementioning

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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Section: Proposed Methodsmentioning

confidence: 99%

Section: Efficient Channel and Spatial Attention Modulementioning

confidence: 99%

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

Ahmed,

Xin,

Lizhuang

2024

IET Image Processing

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“…In recent years, few-shot learning has been applied in various fields. Zhang et al [22] propose a soft distribution-aware few-shot learning strategy to segment tumors from magnetic resonance imaging data in low-resource scenarios. Feng et al [23] propose a class-adaptive framework based on MAML to address few-shot anomaly detection in encrypted traffic.…”

Section: Few-shot Learningmentioning

confidence: 99%

Knowledge-enhanced prototypical network with class cluster loss for few-shot relation classification

Liu

et al. 2023

PLoS ONE

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Few-shot Relation Classification identifies the relation between target entity pairs in unstructured natural language texts by training on a small number of labeled samples. Recent prototype network-based studies have focused on enhancing the prototype representation capability of models by incorporating external knowledge. However, the majority of these works constrain the representation of class prototypes implicitly through complex network structures, such as multi-attention mechanisms, graph neural networks, and contrastive learning, which constrict the model’s ability to generalize. In addition, most models with triplet loss disregard intra-class compactness during model training, thereby limiting the model’s ability to handle outlier samples with low semantic similarity. Therefore, this paper proposes a non-weighted prototype enhancement module that uses the feature-level similarity between prototypes and relation information as a gate to filter and complete features. Meanwhile, we design a class cluster loss that samples difficult positive and negative samples and explicitly constrains both intra-class compactness and inter-class separability to learn a metric space with high discriminability. Extensive experiments were done on the publicly available dataset FewRel 1.0 and 2.0, and the results show the effectiveness of the proposed model.

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“…To further enhance the performance of U-Net in BG segmentation, researchers have introduced additional mechanisms or tricks to the original U-Net architecture. For instance, Zhang et al [20] incorporated residual connections into U-Net, allowing the network to extract deep-level features from MRI data. Zhang et al [21] introduced attention mechanisms into U-Net, enabling the network to focus on relevant features and thereby improving the segmentation accuracy of BG.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Zhang et al. [20] incorporated residual connections into U‐Net, allowing the network to extract deep‐level features from MRI data. Zhang et al.…”

Section: Introductionmentioning

confidence: 99%

WD‐UNeXt: Weight loss function and dropout U‐Net with ConvNeXt for automatic segmentation of few shot brain gliomas

Yin

Gao

Gong

et al. 2023

IET Image Processing

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Accurate segmentation of brain gliomas (BG) is a crucial and challenging task for effective treatment planning in BG therapy. This study presents the weight loss function and dropout U‐Net with ConvNeXt block (WD‐UNeXt), which precisely segments BG from few shot MRI. The ConvNeXt block, which comprises the main body of the network, is a structure that can extract more detailed features from images. The weight loss function addresses the issue of category imbalance, thereby enhancing the network's ability to achieve more precise segmentation. The training set of BraTS2019 was used to train the network and apply it to test data. Dice similarity coefficient (DSC), sensitivity (Sen), specificity (Spec) and Hausdorff distance (HD) were used to assess the performance of the method. The experimental results demonstrate that the DSC of whole tumour, tumour core and enhancing tumour reached 0.934, 0.911 and 0.851, respectively. Sen of the sub‐regions achieved 0.922, 0.911 and 0.867. Spec and HD reached 1.000, 1.000, 1.000 and 3.224, 2.990, 2.844, respectively. Compared with the performance of state‐of‐the‐art methods, the DSC and HD of WD‐UNeXt were improved to varying degrees. Therefore, this method has considerable potential for the segmentation of BG.

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Stroke Lesion Segmentation from Low-Quality and Few-Shot MRIs via Similarity-Weighted Self-ensembling Framework

Cited by 7 publications

References 20 publications

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

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

Knowledge-enhanced prototypical network with class cluster loss for few-shot relation classification

WD‐UNeXt: Weight loss function and dropout U‐Net with ConvNeXt for automatic segmentation of few shot brain gliomas

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