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 …
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 …
An appraisal of the performance of AI tools for chronic stroke lesion segmentation
Automated demarcation of stoke lesions from monospectral magnetic resonance imaging
scans is extremely useful for diverse research and clinical applications, including lesion …
scans is extremely useful for diverse research and clinical applications, including lesion …
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 …
W-Net: A boundary-enhanced segmentation network for stroke lesions
Z Wu, X Zhang, F Li, S Wang, L Huang, J Li - Expert Systems with …, 2023 - Elsevier
Accurate lesion segmentation is a critical technology basis for the treatment and prognosis
of stroke. Stroke lesion segmentation suffers from complex background and noise interferes …
of stroke. Stroke lesion segmentation suffers from complex background and noise interferes …
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 …
MI-UNet: multi-inputs UNet incorporating brain parcellation for stroke lesion segmentation from T1-weighted magnetic resonance images
Stroke is a serious manifestation of various cerebrovascular diseases and one of the most
dangerous diseases in the world today. Volume quantification and location detection of …
dangerous diseases in the world today. Volume quantification and location detection of …
Segmentation of leukocyte by semantic segmentation model: A deep learning approach
RM Roy, PM Ameer - Biomedical Signal Processing and Control, 2021 - Elsevier
In diagnostic research, analysis of blood micrographs has emerged as one of the relevant
techniques for identifying various blood-related diseases. Analysis of white blood cells using …
techniques for identifying various blood-related diseases. Analysis of white blood cells using …
[HTML][HTML] SAN-Net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization
There are considerable interests in automatic stroke lesion segmentation on magnetic
resonance (MR) images in the medical imaging field, as stroke is an important …
resonance (MR) images in the medical imaging field, as stroke is an important …
[HTML][HTML] Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network
N Tomita, S Jiang, ME Maeder, S Hassanpour - NeuroImage: clinical, 2020 - Elsevier
In this paper, we demonstrate the feasibility and performance of deep residual neural
networks for volumetric segmentation of irreversibly damaged brain tissue lesions on T1 …
networks for volumetric segmentation of irreversibly damaged brain tissue lesions on T1 …