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Comparative performance analysis of simple U-Net, residual attention U-Net, and VGG16-U-Net for inventory inland water bodies

Applied Computing and Geosciences, 2023
Inland water bodies play a vital role at all scales in the terrestrial water balance and Earth’s climate variability. Thus, an inventory of inland waters is crucially important for hydrologic and ecological studies and management. Therefore, the main aim
Ali Ghaznavi, Mohammadmehdi Saberioon, Jakub Brom, Sibylle Itzerott   +3 more
doaj   +5 more sources

Chaining a U-Net With a Residual U-Net for Retinal Blood Vessels Segmentation [PDF]

IEEE Access, 2020
Retina images are the only non-invasive way of accessing the cardiovascular system, offering us a means of observing patterns such as microaneurysms, hemorrhages and the vasculature structure which can be used to diagnose a variety of diseases.
Gendry Alfonso Francia, Carlos Pedraza, Marco Aceves, Saul Tovar-Arriaga   +3 more
doaj   +3 more sources

Scale Equivariant U-Net [PDF]

CoRR, 2022
In neural networks, the property of being equivariant to transformations improves generalization when the corresponding symmetry is present in the data. In particular, scale-equivariant networks are suited to computer vision tasks where the same classes of objects appear at different scales, like in most semantic segmentation tasks.
Sangalli, Mateus, Blusseau, Samy, Velasco-Forero, Santiago, Angulo, Jesus   +3 more
core   +6 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   +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

Deep Learning Techniques for Quantification of Tumour Necrosis in Post-neoadjuvant Chemotherapy Osteosarcoma Resection Specimens for Effective Treatment Planning

Acta Informatica Pragensia, 2023
Osteosarcoma is a high-grade malignant bone tumour for which neoadjuvant chemotherapy is a vital component of the treatment plan. Chemotherapy brings about the death of tumour tissues, and the rate of their death is an essential factor in deciding on ...
T. S. Saleena, P. Muhamed Ilyas, V. M. Kutty Sajna, A. K. M. Bahalul Haque   +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 ...
Hongyang Gao, Shuiwang Ji
openaire   +6 more sources

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

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

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, Elagiri Ramalingam Rajkumar, Mattias P. Heinrich   +4 more
openaire   +2 more sources