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Article
Computer Science, Software Engineering
Yuanyuan Li et al.
Summary: This paper proposes a dual encoding-decoding structure of X-shaped network (X-Net) that integrates the characteristics of CNNs and Transformer. It can serve as a good alternative to the traditional pure convolutional medical image segmentation network.
Article
Computer Science, Artificial Intelligence
Zhiqin Zhu et al.
Summary: In this paper, a brain tumor segmentation method based on the fusion of deep semantics and edge information in multimodal MRI is proposed. The method utilizes Swin Transformer for semantic feature extraction, introduces a shifted patch tokenization strategy, and designs an edge spatial attention block and a multi-feature inference block based on graph convolution for feature enhancement and fusion. The experimental results demonstrate that the proposed method outperforms other methods in brain tumor segmentation.
INFORMATION FUSION
(2023)
Article
Biology
Zhou Ma et al.
Summary: This paper proposes an axial Transformer and feature enhancement-based CNN (ATFE-Net) for ultrasound breast mass segmentation. The ATFE-Net utilizes an axial Transformer module and a Transformer-based feature enhancement module to capture long-range dependencies and enhance feature representation. Experimental results demonstrate that the ATFE-Net outperforms several state-of-the-art methods on breast ultrasound datasets.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Interdisciplinary Applications
Xianyu He et al.
Summary: Medical image segmentation is a crucial task in computer-aided diagnosis, and this paper proposes a cloud-based method that leverages multi-feature extraction and interactive fusion to address the limitations of local computing power and the inability of traditional CNNs to extract global features. The proposed approach combines Transformer and CNNs and introduces an interactive fusion attention module to improve segmentation accuracy. Experimental results on multiple medical image datasets validate the effectiveness and progress of the proposed method.
SIMULATION MODELLING PRACTICE AND THEORY
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Moein Heidari et al.
Summary: Convolutional neural networks (CNNs) are widely used for medical image segmentation. However, their ability to model long-range dependencies and spatial correlations is limited due to the nature of convolution operation. Transformers were developed to address this issue, but they struggle to capture low-level features. In this paper, we propose HiFormer, a novel method that combines the strengths of CNN and transformer for efficient medical image segmentation. Extensive experiments demonstrate the effectiveness of HiFormer over other methods in terms of computational complexity and quality of results.
2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV)
(2023)
Article
Engineering, Biomedical
Yu Yan et al.
Summary: In this paper, an Attention Enhanced U-net with hybrid dilated convolution (AE U-net with HDC) model was proposed to segment breast tumors in ultrasound images, achieving higher accuracy and IOU values through the addition of a new loss function and integration of HDC modules.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Huisi Wu et al.
Summary: The study introduces a novel skin lesion segmentation method named FAT-Net, which integrates transformer branch and feature adaptation module to capture long-range dependencies and enhance feature fusion. Experimental results demonstrate the superior accuracy and inference speed of FAT-Net on four public datasets compared to state-of-the-art methods.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Computer Science, Artificial Intelligence
Meng Louv et al.
Summary: This work focuses on improving automated semantic segmentation in breast ultrasound imaging by enhancing contextual relationships between encoder and decoder features through two lightweight context refinement blocks and a novel multi-level context refinement network (MCRNet). Experimental results show the efficacy of the proposed method in achieving fully automated semantic segmentation in ultrasound imaging.
Article
Neurosciences
Yang Xu et al.
Summary: In the field of medical image segmentation, traditional solutions mainly adopt convolutional neural networks (CNNs). This paper proposes a hybrid feature extraction network that combines CNNs and Transformer to better utilize global information for feature extraction and improve the segmentation performance of medical images. Additionally, a multi-dimensional statistical feature extraction module is also introduced to enhance low-dimensional texture features and further improve the segmentation results.
FRONTIERS IN NEUROSCIENCE
(2022)
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Acoustics
Peng Tang et al.
Summary: Automated breast ultrasound image segmentation is crucial for computer-aided diagnosis of breast tumors. The FPNN-TMEL method proposed in this article combines a feature pyramid nonlocal network and transform modal ensemble learning for accurate segmentation of breast tumors in ultrasound images. Evaluation on two datasets demonstrates superior performance in segmentation accuracy compared to other state-of-the-art methods.
IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Cheng Xue et al.
Summary: This paper presents a method for breast ultrasound lesion segmentation using deep convolutional neural networks and its advantages. Experimental results demonstrate the superior performance of the network in this task and also show good results in ultrasound prostate segmentation.
MEDICAL IMAGE ANALYSIS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Jeya Maria Jose Valanarasu et al.
Summary: Deep convolutional neural networks have been widely adopted in medical image segmentation, but lack understanding of long-range dependencies due to inherent biases in convolutional architectures. Transformer-based architectures leverage self-attention mechanism to encode long-range dependencies, motivating the exploration of transformer solutions for medical image segmentation tasks.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT I
(2021)
Article
Biology
Wilfrido Gomez-Flores et al.
COMPUTERS IN BIOLOGY AND MEDICINE
(2020)
Article
Multidisciplinary Sciences
Walid Al-Dhabyani et al.
Article
Computer Science, Interdisciplinary Applications
Zaiwang Gu et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2019)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yuzhou Hu et al.
Article
Computer Science, Artificial Intelligence
Min Xian et al.
PATTERN RECOGNITION
(2018)
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Computer Science, Information Systems
Moi Hoon Yap et al.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
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Proceedings Paper
Computer Science, Artificial Intelligence
Rania Almajalid et al.
2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA)
(2018)
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Computer Science, Artificial Intelligence
Kaiming He et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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Computer Science, Artificial Intelligence
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PATTERN RECOGNITION
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Computer Science, Artificial Intelligence
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PATTERN RECOGNITION
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