Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
A review of deep learning methods for semantic segmentation of remote sensing imagery
X Yuan, J Shi, L Gu - Expert Systems with Applications, 2021 - Elsevier
Semantic segmentation of remote sensing imagery has been employed in many
applications and is a key research topic for decades. With the success of deep learning …
applications and is a key research topic for decades. With the success of deep learning …
Unext: Mlp-based rapid medical image segmentation network
JMJ Valanarasu, VM Patel - … conference on medical image computing and …, 2022 - Springer
UNet and its latest extensions like TransUNet have been the leading medical image
segmentation methods in recent years. However, these networks cannot be effectively …
segmentation methods in recent years. However, these networks cannot be effectively …
FAT-Net: Feature adaptive transformers for automated skin lesion segmentation
Skin lesion segmentation from dermoscopic image is essential for improving the quantitative
analysis of melanoma. However, it is still a challenging task due to the large scale variations …
analysis of melanoma. However, it is still a challenging task due to the large scale variations …
Ds-transunet: Dual swin transformer u-net for medical image segmentation
Automatic medical image segmentation has made great progress owing to powerful deep
representation learning. Inspired by the success of self-attention mechanism in transformer …
representation learning. Inspired by the success of self-attention mechanism in transformer …
SwinBTS: A method for 3D multimodal brain tumor segmentation using swin transformer
Y Jiang, Y Zhang, X Lin, J Dong, T Cheng, J Liang - Brain sciences, 2022 - mdpi.com
Brain tumor semantic segmentation is a critical medical image processing work, which aids
clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural …
clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural …
Transfuse: Fusing transformers and cnns for medical image segmentation
Medical image segmentation-the prerequisite of numerous clinical needs-has been
significantly prospered by recent advances in convolutional neural networks (CNNs) …
significantly prospered by recent advances in convolutional neural networks (CNNs) …
Novel visual crack width measurement based on backbone double-scale features for improved detection automation
Y Tang, Z Huang, Z Chen, M Chen, H Zhou… - Engineering …, 2023 - Elsevier
State-of-the-art machine-vision systems have limitations associated with crack width
measurements. The sample points used to describe the crack width are often subjectively …
measurements. The sample points used to describe the crack width are often subjectively …
A global inventory of photovoltaic solar energy generating units
Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since
2009 1. Energy system projections that mitigate climate change and aid universal energy …
2009 1. Energy system projections that mitigate climate change and aid universal energy …
Medical image segmentation review: The success of u-net
Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …