Transattunet: Multi-level attention-guided u-net with transformer for medical image segmentation
Accurate segmentation of organs or lesions from medical images is crucial for reliable
diagnosis of diseases and organ morphometry. In recent years, convolutional encoder …
diagnosis of diseases and organ morphometry. In recent years, convolutional encoder …
Attention-guided pyramid context network for polyp segmentation in colonoscopy images
Recently, deep convolutional neural networks (CNNs) have provided us an effective tool for
automated polyp segmentation in colonoscopy images. However, most CNN-based …
automated polyp segmentation in colonoscopy images. However, most CNN-based …
BCS-Net: Boundary, context, and semantic for automatic COVID-19 lung infection segmentation from CT images
The spread of COVID-19 has brought a huge disaster to the world, and the automatic
segmentation of infection regions can help doctors to make diagnosis quickly and reduce …
segmentation of infection regions can help doctors to make diagnosis quickly and reduce …
CFU-Net: A coarse-fine U-Net with multi-level attention for medical image segmentation
H Yin, Y Shao - IEEE Transactions on Instrumentation and …, 2023 - ieeexplore.ieee.org
The U-Net has achieved great success in medical image segmentation. Most U-Nets follow
the encoding–decoding-decision inference path and propagate the features from encoding …
the encoding–decoding-decision inference path and propagate the features from encoding …
CPAD-Net: Contextual parallel attention and dilated network for liver tumor segmentation
X Wang, S Wang, Z Zhang, X Yin, T Wang… - … Signal Processing and …, 2023 - Elsevier
Liver cancer is one of the leading causes of cancer death. Accurate and automatic liver
tumor segmentation methods are urgent needs in clinical practice. Currently, Fully …
tumor segmentation methods are urgent needs in clinical practice. Currently, Fully …
PIE-ARNet: Prior image enhanced artifact removal network for limited-angle DECT
Dual-energy computed tomography (DECT) is of great clinical significance because it can
simultaneously visualize the internal structure of the scanned object and provide material …
simultaneously visualize the internal structure of the scanned object and provide material …
COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold
The ground glass opacity (GGO) of the lung is one of the essential features of COVID-19.
The GGO in computed tomography (CT) images has various features and low-intensity …
The GGO in computed tomography (CT) images has various features and low-intensity …
Toward automated right ventricle segmentation via edge feature-induced self-attention multiscale feature aggregation full convolution network
J Liu, M Li, Q Gao, S Gong, Z Tang… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
In the field of cardiac magnetic resonance (MR) image analysis, the accurate segmentation
of right ventricle (RV) regions plays an important role in the quantitative examination and …
of right ventricle (RV) regions plays an important role in the quantitative examination and …
Partition-a-medical-image: Extracting multiple representative sub-regions for few-shot medical image segmentation
Few-shot medical image segmentation (FSMIS) is a more promising solution for medical
image segmentation tasks where high-quality annotations are naturally scarce. However …
image segmentation tasks where high-quality annotations are naturally scarce. However …
MADRU-Net: Multi-Scale Attention-based Cardiac MRI Segmentation using Deep Residual U-Net
Treatment success for atrial fibrillation (AF) has been suboptimal until now, even though it is
among the most frequent types of sustained atrial arrhythmia. Magnetic resonance imaging …
among the most frequent types of sustained atrial arrhythmia. Magnetic resonance imaging …