Results 21 to 30 of about 8,230,666 (286)
Attention recurrent residual U-Net for predicting pixel-level crack widths in concrete surfaces [PDF]
, 2022 Cracks in concrete structures are one of the most important indicators of structural damage, and it is a necessity to detect and measure cracks for ensuring safety and integrity of concrete structures.
Palaniswami, Marimuthu +4 more
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Attention U-Net as a surrogate model for groundwater prediction [PDF]
Advances in Water Resources, 2022 Numerical simulations of groundwater flow are used to analyze and predict the response of an aquifer system to its change in state by approximating the solution of the fundamental groundwater physical equations. The most used and classical methodologies, such as Finite Difference (FD) and Finite Element (FE) Methods, use iterative solvers which are ...
Taccari, ML +5 more
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FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation [PDF]
, 2023 This contribution presents a deep learning method for the segmentation of prostate zones in MRI images based on U-Net using additive and feature pyramid attention modules, which can improve the workflow of prostate cancer detection and diagnosis.
Gonzalez-Mendoza, Miguel +4 more
core +4 more sources
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
doaj +1 more source
Breast Cancer Image Semantic Segmentation with Attention U-Net [PDF]
, 2023 Semantic segmentation is to segment objects in an image into meaningful units. Among them, the basic idea of U-Net is to use low-dimensional as well as high-dimensional information to extract image features and enable accurate location identification. In
Kim, Young-Chae +9 more
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SCAU-Net: Spatial-Channel Attention U-Net for Gland Segmentation
Frontiers in Bioengineering and Biotechnology, 2020 With the development of medical technology, image semantic segmentation is of great significance for morphological analysis, quantification, and diagnosis of human tissues. However, manual detection and segmentation is a time-consuming task.
Peng Zhao +4 more
doaj +1 more source
Residual Edge Attention in U-Net for Brain Tumour Segmentation [PDF]
, 2023 Identification and delineation of the tumour area in images of the brain constitute the crucial job of brain tumour segmentation in medical imaging. This task is crucial for diagnosis, treatment organizing, and keeping a track of brain tumours.
Kumar, E. Kiran +9 more
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Bilateral U‐Net semantic segmentation with spatial attention mechanism
CAAI Transactions on Intelligence Technology, 2023 Aiming at the problem that the existing models have a poor segmentation effect on imbalanced data sets with small‐scale samples, a bilateral U‐Net network model with a spatial attention mechanism is designed. The model uses the lightweight MobileNetV2 as
Guangzhe Zhao +3 more
doaj +1 more source
Chan-Vese Attention U-Net: An Attention Mechanism for Robust Segmentation
, 2023 When studying the results of a segmentation algorithm using convolutional neural networks, one wonders about the reliability and consistency of the results. This leads to questioning the possibility of using such an algorithm in applications where there is little room for doubt.
Nicolas Makaroff, Laurent D. Cohen
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GAU U-Net for multiple sclerosis segmentation
Alexandria Engineering Journal, 2023 Multiple sclerosis is an auto immune disease which affects the brain and nervous system. A total of 2.8 million people are estimated to live with Multiple sclerosis worldwide (35.9 per 100,000 population).
Roba Gamal, Hoda Barka, Mayada Hadhoud
doaj +1 more source