Results 11 to 20 of about 295,660 (323)
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 We consider the problem of representation learning for graph data. Convolutional neural networks can naturally operate on images, but have significant challenges in dealing with graph data. Given images are special cases of graphs with nodes lie on 2D lattices, graph embedding tasks have a natural correspondence with image pixel-wise prediction tasks ...
Gao, Hongyang, Ji, Shuiwang
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Chimeric U-Net – Modifying the standard U-Net towards Explainability
Artificial Intelligence, 2022 Healthcare guided by semantic segmentation has the potential to improve our quality of life through early and accurate disease detection. Convolutional Neural Networks, especially the U-Net-based architectures, are currently the state-of-the-art learningbased segmentation methods and have given unprecedented performances. However, their decision-making
Kenrick Schulze +3 more
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Attention-augmented U-Net (AA-U-Net) for semantic segmentation
Signal, Image and Video Processing, 2022 Deep learning-based image segmentation models rely strongly on capturing sufficient spatial context without requiring complex models that are hard to train with limited labeled data. For COVID-19 infection segmentation on CT images, training data are currently scarce. Attention models, in particular the most recent self-attention methods, have shown to
Kumar T. Rajamani +4 more
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Sensors, 2022 Building segmentation is crucial for applications extending from map production to urban planning. Nowadays, it is still a challenge due to CNNs’ inability to model global context and Transformers’ high memory need.
Batuhan Sariturk, Dursun Zafer Seker
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Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach
Sensors, 2021 The automatic segmentation of the pancreatic cyst lesion (PCL) is essential for the automated diagnosis of pancreatic cyst lesions on endoscopic ultrasonography (EUS) images. In this study, we proposed a deep-learning approach for PCL segmentation on EUS
Seok Oh +3 more
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Scientific Reports, 2022 Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies.
Kang Hsu +12 more
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Frontiers in Oncology, 2021 Few studies have reported the reproducibility and stability of ultrasound (US) images based radiomics features obtained from automatic segmentation in oncology. The purpose of this study is to study the accuracy of automatic segmentation algorithms based
Juebin Jin +9 more
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Journal of Medical Internet Research, 2022 BackgroundDetection and quantification of intra-abdominal free fluid (ie, ascites) on computed tomography (CT) images are essential processes for finding emergent or urgent conditions in patients.
Hoon Ko +7 more
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Segmentation and recognition of breast ultrasound images based on an expanded U-Net.
PLoS ONE, 2021 This paper establishes a fully automatic real-time image segmentation and recognition system for breast ultrasound intervention robots. It adopts the basic architecture of a U-shaped convolutional network (U-Net), analyses the actual application ...
Yanjun Guo +3 more
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Local brain-age: A U-Net model [PDF]
Frontiers in Aging Neuroscience, 2021 AbstractWe propose a new framework for estimating neuroimaging-derived “brain-age” at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing.
Sebastian G. Popescu +6 more
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