Multiscale Attention Aggregation Network for 2D Vessel Segmentation

Li, Wentao; Yang, Huihua; Tian, Tong; Pan, Xipeng; Xu, Weijin

doi:10.1109/icassp43922.2022.9747207

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Although deep supervision is widely used to obtain better performance, some global context information detailed information may still be lost due to the semantic gaps existing among feature layers of the decoder, which eventually makes a misleading for the following denser prediction in the decoder. Inspired MAA-Net [8], to further utilize context information and achieve a more effective output feature fusion in our decoder, we propose a multi-level semantic supervision (MSS) module to address this problem. The semantic supervision module, as shown in Fig.…”

Section: Multi-level Semantic Supervisionmentioning

confidence: 99%

CA-Net: Context Aggregation Network for Nuclei Classification in Histopathology Image

Xiao

et al. 2023

J. Phys.: Conf. Ser.

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Accurately classifying nuclei in histopathology images is essential for cancer diagnosis and prognosis. However, due to the touching nuclei, nucleus shape variation, background complexity, and image artifacts, end-to-end nucleus classification is still difficult and challenging. In this manuscript, we propose a context aggregation network (CA-Net) for nuclei classification by fusing global contextual information which is critical for classifying nuclei in histopathology images. Specifically, we propose a multi-level semantic supervision (MSS) module focusing on extracting multi-scale context information by varying three different kernel sizes, and dynamically aggregating the context information from high to low level. Furthermore, we employ the GPG and SAPF modules in encoder and decoder networks to exact and aggregate global context information. Finally, the proposed network is verified on a mainstream nuclei classification image datasets (PanNuke) and achieves an improved global accuracy of 0.816. Our proposed MSS module can be easily transferred into any UNet-liked architecture as a deep supervision mechanism.

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Section: Multi-level Semantic Supervisionmentioning

confidence: 99%

CA-Net: Context Aggregation Network for Nuclei Classification in Histopathology Image

Xiao

et al. 2023

J. Phys.: Conf. Ser.

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CAS‐GAN: A Novel Generative Adversarial Network‐Based Architecture for Coronary Artery Segmentation

Hamdi,

Kerkeni,

Abdallah

et al. 2024

Int J Imaging Syst Tech

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Accurate and automated segmentation of x‐ray coronary angiography (XRCA) is crucial for both diagnosing and treating coronary artery diseases. Despite the outstanding results achieved by deep learning (DL)‐based methods in this area, this task remains challenging due to several factors such as poor image quality, the presence of motion artifacts, and inherent variability in vessel structure sizes. To address this challenge, this paper introduces a novel GAN‐based architecture for coronary artery segmentation using XRCA images. This architecture includes a novel U‐Net variant with two types of self‐attention blocks in the generator segment. An auxiliary path connects the attention block and the prediction block to enhance feature generalization, improving vessel structure delineation, especially thin vessels in low‐contrast regions. In parallel, the discriminator network employs a residual CNN with similar attention blocks for balanced performance and improved predictive capabilities. With a streamlined 6.74 M parameters, the resulting architecture surpasses existing methods in efficiency. We assess its efficacy on three coronary artery datasets: our private “CORONAR,” and the public “DCA1” and “CHUAC” datasets. Empirical results showcase our model's superiority across these datasets, utilizing both original and preprocessed images. Notably, our proposed architecture achieves the highest F1‐score of 0.7972 for the CHUAC dataset, 0.8245 for the DCA1 dataset, and 0.8333 for the CORONAR dataset.

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Spatio-Temporal correspondence attention network for vessel segmentation in X-ray coronary angiography

Gao,

Ai,

Wang

et al. 2025

Biomedical Signal Processing and Control

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Multiscale Attention Aggregation Network for 2D Vessel Segmentation

Cited by 3 publications

References 15 publications

CA-Net: Context Aggregation Network for Nuclei Classification in Histopathology Image

CA-Net: Context Aggregation Network for Nuclei Classification in Histopathology Image

CAS‐GAN: A Novel Generative Adversarial Network‐Based Architecture for Coronary Artery Segmentation

Spatio-Temporal correspondence attention network for vessel segmentation in X-ray coronary angiography

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