Technologies, 2022 Detecting and localizing buildings is of primary importance in urban planning tasks. Automating the building extraction process, however, has become attractive given the dominance of Convolutional Neural Networks (CNNs) in image classification tasks.
Anastasios Temenos +3 more
doaj +1 more source
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
doaj +1 more source
An Automatic Nuclei Image Segmentation Based on Multi-Scale Split-Attention U-Net
, 2021 Nuclei segmentation is an important step in the task of medical image analysis. Nowadays, deep learning techniques based on Convolutional Neural Networks (CNNs) have become prevalent methods in nuclei segmentation.
Wenting Duan (17157046) +1 more
core +3 more sources
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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Attention-based U-Net for image demoiréing
Machine Graphics & Vision, 2022 Image demoiréing is a particular example of a picture restoration problem. Moiré is an interference pattern generated by overlaying similar but slightly offset templates.
Tomasz M. Lehmann
doaj +1 more source
ASCU-Net: Attention Gate, Spatial and Channel Attention U-Net for Skin Lesion Segmentation
Diagnostics, 2021 Segmentation of skin lesions is a challenging task because of the wide range of skin lesion shapes, sizes, colors, and texture types. In the past few years, deep learning networks such as U-Net have been successfully applied to medical image segmentation
Xiaozhong Tong +5 more
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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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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
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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
core +1 more source
AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images
, 2021 The prerequisite for the use of remote sensing images is that their security must be guaranteed. As a special subset of perceptual hashing, subject-sensitive hashing overcomes the shortcomings of the existing perceptual hashing that cannot distinguish ...
Yue Zeng +5 more
core +1 more source