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Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021Over the past decade, Deep Convolutional Neural Networks have been widely adopted for medical image segmentation and shown to achieve adequate performance.
Jeya Maria Jose Valanarasu +3 more
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TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021Medical image segmentation - the prerequisite of numerous clinical needs - has been significantly prospered by recent advances in convolutional neural networks (CNNs). However, it exhibits general limitations on modeling explicit long-range relation, and
Yundong Zhang, Huiye Liu, Qiang Hu
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Medical Image Segmentation via Cascaded Attention Decoding
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2023Transformers have shown great promise in medical image segmentation due to their ability to capture long-range dependencies through self-attention. However, they lack the ability to learn the local (contextual) relations among pixels.
M. Rahman, R. Marculescu
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H2Former: An Efficient Hierarchical Hybrid Transformer for Medical Image Segmentation
IEEE Transactions on Medical Imaging, 2023Accurate medical image segmentation is of great significance for computer aided diagnosis. Although methods based on convolutional neural networks (CNNs) have achieved good results, it is weak to model the long-range dependencies, which is very important
Along He +5 more
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IEEE Pulse, 2011
Since the discovery of the X-ray radiation by Wilhelm Conrad Roentgen in 1895, the field of medical imaging has developed into a huge scientific discipline. The analysis of patient data acquired by current image modalities, such as computerized tomography (CT), magnetic resonance tomography (MRT), positron emission tomography (PET), or ultrasound (US),
Felix, Ritter +6 more
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Since the discovery of the X-ray radiation by Wilhelm Conrad Roentgen in 1895, the field of medical imaging has developed into a huge scientific discipline. The analysis of patient data acquired by current image modalities, such as computerized tomography (CT), magnetic resonance tomography (MRT), positron emission tomography (PET), or ultrasound (US),
Felix, Ritter +6 more
openaire +2 more sources
2016
Imaging is one of the most important sources of clinically observable evidence that provides broad coverage, can provide insight on low-level scale properties, is noninvasive, has few side effects, and can be performed frequently. Thus, imaging data provides a viable observable that can facilitate the instantiation of a theoretical understanding of a ...
William, Hsu +2 more
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Imaging is one of the most important sources of clinically observable evidence that provides broad coverage, can provide insight on low-level scale properties, is noninvasive, has few side effects, and can be performed frequently. Thus, imaging data provides a viable observable that can facilitate the instantiation of a theoretical understanding of a ...
William, Hsu +2 more
openaire +2 more sources
Physics in Medicine and Biology, 2001
Radiological images are increasingly being used in healthcare and medical research. There is, consequently, widespread interest in accurately relating information in the different images for diagnosis, treatment and basic science. This article reviews registration techniques used to solve this problem, and describes the wide variety of applications to ...
Hill, D L G +3 more
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Radiological images are increasingly being used in healthcare and medical research. There is, consequently, widespread interest in accurately relating information in the different images for diagnosis, treatment and basic science. This article reviews registration techniques used to solve this problem, and describes the wide variety of applications to ...
Hill, D L G +3 more
openaire +2 more sources
ResNet and its application to medical image processing: Research progress and challenges
Comput. Methods Programs Biomed., 2023BACKGROUND AND OBJECTIVE Deep learning, a novel approach and subset of machine learning, has drawn a growing amount of attention from computer vision researchers in recent years.
Wanni Xu, You Fu, Dongmei Zhu
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Medical Image Anal.
Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face limitations in modeling long-range dependencies. To address this, Transformers designed for sequence-to-sequence predictions have been integrated into ...
Jieneng Chen +15 more
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Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face limitations in modeling long-range dependencies. To address this, Transformers designed for sequence-to-sequence predictions have been integrated into ...
Jieneng Chen +15 more
semanticscholar +1 more source
Media images and medical images
Social Science & Medicine (1967), 1975Abstract The study sought to examine the image of women portrayed in drug advertisements and how that image contrasts with the portrayal of men. Special attention was given to advertisements for mood-modifying drugs since women are the majority of users of such drugs. Content analysis was performed on nearly 500 drug advertisements in a sample drawn
Andrea Mant, Dorothy Broom Darroch
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