WRANet: wavelet integrated residual attention U-Net network for medical image segmentation

Zhao, Yawu; Wang, Shudong; Zhang, Yulin; Qiao, Sibo; Zhang, M

doi:10.1007/s40747-023-01119-y

Cited by 7 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the main semantic information is conveyed in the LF component, previous studies have often used the LF component alone in certain tasks (Li et al, 2020;Zhao et al, 2023). However, researchers have attempted to aggregate HF components with methods such as concatenation (Liu et al, 2018;de Souza Brito et al, 2021;Liu et al, 2021), maximum (Ramamonjisoa et al, 2021), or element-wise addition (Zhou et al, 2023), and incorporate them into DNNs to improve model performance.…”

Section: Analysis Of Different Frequency Componentsmentioning

confidence: 99%

Semi-supervised learning advances species recognition for aquatic biodiversity monitoring

Ma,

Wei,

Zhu

et al. 2024

Front. Mar. Sci.

View full text Add to dashboard Cite

Aquatic biodiversity monitoring relies on species recognition from images. While deep learning (DL) streamlines the recognition process, the performance of these method is closely linked to the large-scale labeled datasets, necessitating manual processing with expert knowledge and consume substantial time, labor, and financial resources. Semi-supervised learning (SSL) offers a promising avenue to improve the performance of DL models by utilizing the extensive unlabeled samples. However, the complex collection environments and the long-tailed class imbalance of aquatic species make SSL difficult to implement effectively. To address these challenges in aquatic species recognition within the SSL scheme, we propose a Wavelet Fusion Network and the Consistency Equilibrium Loss function. The former mitigates the influence of data collection environment by fusing image information at different frequencies decomposed through wavelet transform. The latter improves the SSL scheme by refining the consistency loss function and adaptively adjusting the margin for each class. Extensive experiments are conducted on the large-scale FishNet dataset. As expected, our method improves the existing SSL scheme by up to 9.34% in overall classification accuracy. With the accumulation of image data, the improved SSL method with limited labeled data, shows the potential to advance species recognition for aquatic biodiversity monitoring and conservation.

show abstract

Section: Analysis Of Different Frequency Componentsmentioning

confidence: 99%

Semi-supervised learning advances species recognition for aquatic biodiversity monitoring

Ma,

Wei,

Zhu

et al. 2024

Front. Mar. Sci.

View full text Add to dashboard Cite

show abstract

“…Moreover, the model includes a spatial attention mechanism that allows the network to prioritize important parts of the image while ignoring irrelevant areas. In Zhao, Wang, et al (2023), a wavelet residual attention network (WRANet) for medical image segmentation was proposed. This method involves substituting traditional downsampling modules, such as maximum pooling and average pooling, with a discrete wavelet transform.…”

Section: Related Workmentioning

confidence: 99%

Multi‐focal channel attention for medical image segmentation

Al Jowair,

Alsulaiman,

Muhammad

2024

Expert Systems

View full text Add to dashboard Cite

Medical image segmentation through the use of deep learning is becoming a trend targeting to automate disease detection and provide effective treatment. Traditionally huge manual efforts are associated with medical image processing by medical staff. The wide use of neural networks driven by the progressive advancement in computing power and the availability of training data provides instrumental means to automate medical image processing. A polyp refers to an abnormal growth that can occur in various parts of the body. While the majority of polyps are noncancerous or benign, there are instances where certain types can develop into cancer. Detecting and segmenting polyps is highly valuable for identifying early signs and potentially enabling more effective treatment of colon cancer. In this paper, we introduce a novel method for segmenting polyp images. The proposed method utilizes convolutional neural networks (CNNs) and employs an enhanced attention mechanism. To cater to a more granular attention view, this paper enhances the Convolutional Block Attention Module by introducing the multi‐focal channel attention (MFCA) concept, which we call MFCA. In the proposed MFCA channel attention, focal attention spots allow us to consider scattered areas of interest more effectively. To evaluate the effectiveness of the proposed method, multiple experiments are conducted on five well‐known benchmark datasets for polyp image segmentation: Kvasir, CVC‐Clinic DB, CVC‐Colon DB, CVC‐T, and ETIS‐Larib. Various testing scenarios are examined, including the impact of two focal attention spots and four focal attention spots. The experimental results demonstrated that the proposed method achieved superior performance compared to previous state‐of‐the‐art techniques on two of the benchmark datasets, and ranked second on a third dataset.

show abstract

“…These models show that deep learning is promising for medical image segmentation, and several deep learning-based automatic methods (Park et al 2019, Ueda et al 2019, Dai et al 2020, Jin et al 2020, Bo et al 2021 for detecting IAs via CTA, MRA, and DSA have been proposed. Zhao et al (2023) proposed a residual attention model for extracting multiscale features from MRA images. Yang et al (2022) used point-based 3D neural networks for aneurysm segmentation.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for intracranial aneurysm segmentation using CT angiography

Zheng,

Liu,

Huang

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

Objective: This study aimed to employ a two-stage deep learning method to accurately detect small aneurysms (4-10 mm in size) in computed tomography angiography (CTA) images.Methods: This study included 956 patients from 6 hospitals and a public dataset obtained with 6 CT scanners from different manufacturers. The proposed method consists of two components: a lightweight and fast head region selection (HRS) algorithm and an adaptive 3D nnU-Net network, which is used as the main architecture for segmenting aneurysms. Segments generated by the deep neural network were compared with expert-generated manual segmentation results and assessed using Dice scores.Results: The area under the curve (AUC) exceeded 79% across all datasets. In particular, the precision and AUC reached 85.2% and 87.6%, respectively, on certain datasets. The experimental results demonstrated the promising performance of this approach, which reduced the inference time by more than 50% compared to direct inference without HRS.Conclusion: Compared with a model without HRS, the deep learning approach we developed can accurately segment aneurysms by automatically localizing brain regions and can accelerate aneurysm inference by more than 50%.Keywords: Deep Learning, CT Angiography, Aneurisms

show abstract

WRANet: wavelet integrated residual attention U-Net network for medical image segmentation

Cited by 7 publications

References 47 publications

Semi-supervised learning advances species recognition for aquatic biodiversity monitoring

Semi-supervised learning advances species recognition for aquatic biodiversity monitoring

Multi‐focal channel attention for medical image segmentation

Deep learning for intracranial aneurysm segmentation using CT angiography

Contact Info

Product

Resources

About