Results 21 to 30 of about 6,547,057 (287)

Semantic Segmentation on Panoramic Dental X-Ray Images Using U-Net Architectures

IEEE Access
The field of medical image analysis is in a constant state of evolution, particularly in the challenging tasks of segmenting organs, diseases, and abnormalities. Therefore, in the realm of dental disease diagnosis, image segmentation plays a crucial role
Rafiatul Zannah, Mubtasim Bashar, Rahil Bin Mushfiq, Amitabha Chakrabarty, Shahriar Hossain, Yong Ju Jung   +5 more
doaj   +2 more sources

MSR U-Net: An Improved U-Net Model for Retinal Blood Vessel Segmentation

IEEE Access
For the proper diagnosis and treatment of a variety of retinal conditions, retinal blood vessel segmentation is crucial. Delineation of vessels with varying thicknesses is critical for detecting disease symptoms.
Giri Babu Kande, Logesh Ravi, Nitya Kande, Madhusudana Rao Nalluri, Hossam Kotb, Kareem M. AboRas, Amr Yousef, Yazeed Yasin Ghadi, A. Sasikumar   +8 more
doaj   +2 more sources

UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer [PDF]

AAAI Conference on Artificial Intelligence, 2021
Most recent semantic segmentation methods adopt a U-Net framework with an encoder-decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to model the global multi-scale context: 1) Not each skip connection setting is
Haonan Wang, Peng Cao, Jiaqi Wang, Osmar R Zaiane   +3 more
semanticscholar   +1 more source

On the Exploration of Automatic Building Extraction from RGB Satellite Images Using Deep Learning Architectures Based on U-Net

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, Nikos Temenos, Anastasios Doulamis, Nikolaos Doulamis   +3 more
doaj   +1 more source

Graph U-Nets

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
openaire   +3 more sources

DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation [PDF]

IEEE Transactions on Instrumentation and Measurement, 2021
Automatic medical image segmentation has made great progress owing to powerful deep representation learning. Inspired by the success of self-attention mechanism in transformer, considerable efforts are devoted to designing the robust variants of the ...
Ailiang Lin, Bingzhi Chen, Jiayu Xu, Zheng Zhang, Guangming Lu, David Zhang   +5 more
semanticscholar   +1 more source

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, Felix Peppert, Christof Schütte, Vikram Sunkara   +3 more
openaire   +2 more sources

Medical Image Segmentation Review: The Success of U-Net [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized
Reza Azad, E. K. Aghdam, Amelie Rauland, Yiwei Jia, A. Avval, Afshin Bozorgpour, Sanaz Karimijafarbigloo, J. P. Cohen, Ehsan Adeli, D. Merhof   +9 more
semanticscholar   +1 more source

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, Priya Rani, Hanna Siebert, Rajkumar ElagiriRamalingam, Mattias P. Heinrich   +4 more
openaire   +2 more sources

A Residual-Inception U-Net (RIU-Net) Approach and Comparisons with U-Shaped CNN and Transformer Models for Building Segmentation from High-Resolution Satellite Images

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