Adaptive hypergraph superpixels

Wang, Shaofan; Lan, Jiaqi; Jiang, Lin; Liu, Yukun; Wang, Lichun; Sun, Yanfeng; Yin, Baocai

doi:10.1016/j.displa.2023.102369

Cited by 4 publications

(1 citation statement)

References 59 publications

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“…The network produced pseudo-labels that became the training base for DeepLab [43]. An adaptive hypergraph superpixel segmentation (AHS) [44] for intensity images has been proposed to address the high-order relationships of locally adjacent pixels and the instability of feature range radius. AHS constructed a hypergraph by building the hyperedges with an adaptive neighbourhood scheme.…”

Section: Hypergraph Learningmentioning

confidence: 99%

HGSNet: A hypergraph network for subtle lesions segmentation in medical imaging

Wang,

Zhang,

et al. 2024

IET Image Processing

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Lesion segmentation is a fundamental task in medical image processing, often facing the challenge of subtle lesions. It is important to detect these lesions, even though they can be difficult to identify. Convolutional neural networks, an effective method in medical image processing, often ignore the relationship between lesions, leading to topological errors during training. To tackle topological errors, move is made from pixel‐level to hypergraph representations. Hypergraphs can model lesions as vertices connected by hyperedges, capturing the topology between lesions. This paper introduces a novel dynamic hypergraph learning strategy called DHLS. DHLS allows for the dynamic construction of hypergraphs contingent upon input vertex variations. A hypergraph global‐aware segmentation network, termed HGSNet, is further proposed. HGSNet can capture the key high‐order structure information, which is able to enhance global topology expression. Additionally, a composite loss function is introduced. The function emphasizes the global aspect and the boundary of segmentation regions. The experimental setup compared HGSNet with other advanced models on medical image datasets from various organs. The results demonstrate that HGSNet outperforms other models and achieves state‐of‐the‐art performance on three public datasets.

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Section: Hypergraph Learningmentioning

confidence: 99%