Swin-umamba: Mamba-based unet with imagenet-based pretraining
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
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 …