[HTML][HTML] DCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation
Deep learning architecture with convolutional neural network achieves outstanding success
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …
A review on computer aided diagnosis of acute brain stroke
Amongst the most common causes of death globally, stroke is one of top three affecting over
100 million people worldwide annually. There are two classes of stroke, namely ischemic …
100 million people worldwide annually. There are two classes of stroke, namely ischemic …
Attention gate resU-Net for automatic MRI brain tumor segmentation
Brain tumor segmentation technology plays a pivotal role in the process of diagnosis and
treatment of MRI brain tumors. It helps doctors to locate and measure tumors, as well as …
treatment of MRI brain tumors. It helps doctors to locate and measure tumors, as well as …
A fully automated multimodal MRI-based multi-task learning for glioma segmentation and IDH genotyping
The accurate prediction of isocitrate dehydrogenase (IDH) mutation and glioma
segmentation are important tasks for computer-aided diagnosis using preoperative …
segmentation are important tasks for computer-aided diagnosis using preoperative …
Annotation-efficient deep learning for automatic medical image segmentation
Automatic medical image segmentation plays a critical role in scientific research and
medical care. Existing high-performance deep learning methods typically rely on large …
medical care. Existing high-performance deep learning methods typically rely on large …
Computational approaches for acute traumatic brain injury image recognition
E Lin, EL Yuh - Frontiers in neurology, 2022 - frontiersin.org
In recent years, there have been major advances in deep learning algorithms for image
recognition in traumatic brain injury (TBI). Interest in this area has increased due to the …
recognition in traumatic brain injury (TBI). Interest in this area has increased due to the …
Deep neural architectures for medical image semantic segmentation
MZ Khan, MK Gajendran, Y Lee, MA Khan - IEEE Access, 2021 - ieeexplore.ieee.org
Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …
C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation
Background and objective Breast lesions segmentation is an important step of computer-
aided diagnosis system. However, speckle noise, heterogeneous structure, and similar …
aided diagnosis system. However, speckle noise, heterogeneous structure, and similar …
GCAUNet: A group cross-channel attention residual UNet for slice based brain tumor segmentation
Z Huang, Y Zhao, Y Liu, G Song - Biomedical Signal Processing and …, 2021 - Elsevier
Precise brain tumor segmentation can improve patient prognosis. However, due to the
complicated structure of the human brain, brain tumor segmentation is a challenging task. To …
complicated structure of the human brain, brain tumor segmentation is a challenging task. To …
Brain stroke lesion segmentation using consistent perception generative adversarial network
S Wang, Z Chen, S You, B Wang, Y Shen… - Neural Computing and …, 2022 - Springer
The state-of-the-art deep learning methods have demonstrated impressive performance in
segmentation tasks. However, the success of these methods depends on a large amount of …
segmentation tasks. However, the success of these methods depends on a large amount of …