A multi-channel UNet framework based on SNMF-DCNN for robust heart-lung-sound separation

Wang, Weibo; Qin, Dimei; Wang, Shubo; Yu, F. Richard; Zheng, Yongkang

doi:10.1016/j.compbiomed.2023.107282

Cited by 9 publications

(4 citation statements)

References 37 publications

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“…However, the stacked convolutional operations in its encoding and decoding stages may lead to information loss. To overcome these limitations, researchers have proposed various improved models such as MAS-UNet [ 17 ], U2Net [ 32 ], Multi-UNet [ 33 ], and others. For instance, U-Net++ enhances the capture of multiscale information by introducing cross-scale connections.…”

Section: Related Workmentioning

confidence: 99%

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HDS-Net: Achieving fine-grained skin lesion segmentation using hybrid encoding and dynamic sparse attention

Xue,

Chen,

Liu

et al. 2024

PLoS ONE

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Skin cancer is one of the most common malignant tumors worldwide, and early detection is crucial for improving its cure rate. In the field of medical imaging, accurate segmentation of lesion areas within skin images is essential for precise diagnosis and effective treatment. Due to the capacity of deep learning models to conduct adaptive feature learning through end-to-end training, they have been widely applied in medical image segmentation tasks. However, challenges such as boundary ambiguity between normal skin and lesion areas, significant variations in the size and shape of lesion areas, and different types of lesions in different samples pose significant obstacles to skin lesion segmentation. Therefore, this study introduces a novel network model called HDS-Net (Hybrid Dynamic Sparse Network), aiming to address the challenges of boundary ambiguity and variations in lesion areas in skin image segmentation. Specifically, the proposed hybrid encoder can effectively extract local feature information and integrate it with global features. Additionally, a dynamic sparse attention mechanism is introduced, mitigating the impact of irrelevant redundancies on segmentation performance by precisely controlling the sparsity ratio. Experimental results on multiple public datasets demonstrate a significant improvement in Dice coefficients, reaching 0.914, 0.857, and 0.898, respectively.

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Section: Related Workmentioning

confidence: 99%

“…[32], Multi-UNet [33], and others. For instance, U-Net++ enhances the capture of multiscale information by introducing cross-scale connections.…”

Section: Competing Interestsmentioning

confidence: 99%

HDS-Net: Achieving fine-grained skin lesion segmentation using hybrid encoding and dynamic sparse attention

Xue,

Chen,

Liu

et al. 2024

PLoS ONE

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“…In addition to the sensing method, current research is focused on different types of lung sounds, such as crackling, rhonchi, wheezing and stridor. 20–27 However, the lung sound type is not directly related to the diagnosis of the type of lung disease, and one type of disease is sometimes related to several types of abnormal lung sounds.…”

Section: Introductionmentioning

confidence: 99%

Wearable stethoscope for lung disease diagnosis

Qiu,

Zeng,

Tian

et al. 2024

Sens. Diagn.

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“…Lyu et al proposed a novel multiple-tasking Wasserstein generative adversarial network U-shape network and utilized the attention mechanism to enhance the segmentation accuracy of the generator [12]. Wang et al first proposed the Heart-Lung-Sound classification method SNMF-DCNN and applied U-Net for cardiopulmonary sound separation [13]. Rehman et al proposed a novel tumor segmentation model, BU-Net [14].…”

Section: Introductionmentioning

confidence: 99%

CMP-UNet: A Retinal Vessel Segmentation Network Based on Multi-Scale Feature Fusion

Gu,

Cao,

Wang

et al. 2023

Electronics

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Retinal vessel segmentation plays a critical role in the diagnosis and treatment of various ophthalmic diseases. However, due to poor image contrast, intricate vascular structures, and limited datasets, retinal vessel segmentation remains a long-term challenge. In this paper, based on an encoder–decoder framework, a novel retinal vessel segmentation model called CMP-UNet is proposed. Firstly, the Coarse and Fine Feature Aggregation module decouples and aggregates coarse and fine vessel features using two parallel branches, thus enhancing the model’s ability to extract features for vessels of various sizes. Then, the Multi-Scale Channel Adaptive Fusion module is embedded in the decoder to realize the efficient fusion of cascade features by mining the multi-scale context information from these features. Finally, to obtain more discriminative vascular features and enhance the connectivity of vascular structures, the Pyramid Feature Fusion module is proposed to effectively utilize the complementary information of multi-level features. To validate the effectiveness of the proposed model, it is evaluated on three publicly available retinal vessel segmentation datasets: CHASE_DB1, DRIVE, and STARE. The proposed model, CMP-UNet, reaches F1-scores of 82.84%, 82.55%, and 84.14% on these three datasets, with improvements of 0.76%, 0.31%, and 1.49%, respectively, compared with the baseline. The results show that the proposed model achieves higher segmentation accuracy and more robust generalization capability than state-of-the-art methods.

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A multi-channel UNet framework based on SNMF-DCNN for robust heart-lung-sound separation

Cited by 9 publications

References 37 publications

HDS-Net: Achieving fine-grained skin lesion segmentation using hybrid encoding and dynamic sparse attention

HDS-Net: Achieving fine-grained skin lesion segmentation using hybrid encoding and dynamic sparse attention

Wearable stethoscope for lung disease diagnosis

CMP-UNet: A Retinal Vessel Segmentation Network Based on Multi-Scale Feature Fusion

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