DOLG-NeXt: Convolutional neural network with deep orthogonal fusion of local and global features for biomedical image segmentation
Biomedical image segmentation (BMIS) is an essential yet challenging task for the visual
analysis of biomedical images. Modern deep learning-based architectures, such as UNet …
analysis of biomedical images. Modern deep learning-based architectures, such as UNet …
SECA-Net: Squeezed-and-excitated contextual attention network for medical image segmentation
Accurate medical image sgmentation is critical in the computer-aided diagnosis paradigm,
serving as a crucial pathway for the prevention and treatment of various diseases. In this …
serving as a crucial pathway for the prevention and treatment of various diseases. In this …
Long-tailed object detection for multimodal remote sensing images
J Yang, M Yu, S Li, J Zhang, S Hu - Remote Sensing, 2023 - mdpi.com
With the rapid development of remote sensing technology, the application of convolutional
neural networks in remote sensing object detection has become very widespread, and some …
neural networks in remote sensing object detection has become very widespread, and some …
MSR-Net: Multi-scale residual network based on attention mechanism for pituitary adenoma MRI image segmentation
Q Zhang, X Jiang, X Huang, C Zhou - IEEE Access, 2024 - ieeexplore.ieee.org
Accurate segmentation of pituitary adenoma lesions is essential for effective diagnosis and
treatment planning. However, traditional algorithms struggle with this task due to the …
treatment planning. However, traditional algorithms struggle with this task due to the …
[HTML][HTML] Adaptive edge prior-based deep attention residual network for low-dose CT image denoising
Improving the diagnostic quality of low-dose CT (LDCT) images relies on effective noise
removal. Recent advancements have highlighted the widespread use of deep residual …
removal. Recent advancements have highlighted the widespread use of deep residual …
[PDF][PDF] PulmonU-Net: a semantic lung disease segmentation model leveraging the benefit of multiscale feature concatenation and leaky ReLU
H Mary Shyni, E Chitra - Automatika: časopis za automatiku, mjerenje …, 2024 - hrcak.srce.hr
PulmonU-Net: a semantic lung disease segmentation model leveraging the benefit of multiscale
feature Page 1 Automatika Journal for Control, Measurement, Electronics, Computing and …
feature Page 1 Automatika Journal for Control, Measurement, Electronics, Computing and …
[HTML][HTML] Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis
The carotid artery is a vital blood vessel that supplies oxygenated blood to the brain.
Blockages in this artery can lead to life-threatening illnesses, making accurate diagnosis …
Blockages in this artery can lead to life-threatening illnesses, making accurate diagnosis …
Dual-Stream CoAtNet models for accurate breast ultrasound image segmentation
The CoAtNet deep neural model has been shown to achieve state-of-the-art performance by
stacking convolutional and self-attention layers. In particular, the initial layers of CoAtNet …
stacking convolutional and self-attention layers. In particular, the initial layers of CoAtNet …
HRCUNet: Hierarchical Region Contrastive Learning for Segmentation of Breast Tumors in DCE‐MRI
Segmenting breast tumors from dynamic contrast‐enhanced magnetic resonance images is
a critical step in the early detection and diagnosis of breast cancer. However, this task …
a critical step in the early detection and diagnosis of breast cancer. However, this task …
IEEE BigData Cup 2023 Report: Object Recognition with Muon Tomography Using Cosmic Rays
M Wnuk, J Dziuba, A Janusz… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
We summarize the results of the IEEE BigData 2023 Cup: Object Recognition with Muon
Tomography using Cosmic Rays-a data mining competition organized at the KnowledgePit …
Tomography using Cosmic Rays-a data mining competition organized at the KnowledgePit …