Results 11 to 20 of about 4,013,335 (305)
U-Net_dc: a novel U-Net-based model for endometrial cancer cell image segmentation [PDF]
, 2023 Mutated cells may constitute a source of cancer. As an effective approach to quantifying the extent of cancer, cell image segmentation is of particular importance for understanding the mechanism of the disease, observing the degree of cancer cell lesions,
Zhanlin Ji (14016624) +6 more
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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 ...
Hongyang Gao, Shuiwang Ji
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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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U-Net Model Architecture. [PDF]
, 2022 U-Net architecture used for the experiments in Table 1. D specifies the base level of channels; we considered experiments with D = 2, 4, 8, 16.
Brett W. Larsen (12902597) +1 more
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U-Net epoch-loss curves. [PDF]
, 2022 U-Net epoch-loss curves.
Guoxiong Zhou (12245608) +5 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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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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U-Net Model Performance. [PDF]
, 2022 We trained four U-Net architectures of increasing size by each time doubling the number of channels in each layer. For each model, we report the best performance across 10 trained models on 10,240 test samples.
Brett W. Larsen (12902597) +1 more
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Segmentation of roots in soil with U-Net [PDF]
Plant Methods, 2020 Abstract Background Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in a diverse array of conditions. Phenotyping roots in soil is often challenging due to the roots being difficult to access and the use of time consuming manual methods.
Abraham George Smith +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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