A comprehensive survey on applications of transformers for deep learning tasks
Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …
mechanism to capture contextual relationships within sequential data. Unlike traditional …
Deep learning in hydrology and water resources disciplines: Concepts, methods, applications, and research directions
KP Tripathy, AK Mishra - Journal of Hydrology, 2024 - Elsevier
Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …
recognition within the field of hydrology and water resources applications. Beginning with a …
Fully transformer network for change detection of remote sensing images
Recently, change detection (CD) of remote sensing images have achieved great progress
with the advances of deep learning. However, current methods generally deliver incomplete …
with the advances of deep learning. However, current methods generally deliver incomplete …
A crack-segmentation algorithm fusing transformers and convolutional neural networks for complex detection scenarios
The performance of crack segmentation is influenced by complex scenes, including
irregularly shaped cracks, complex image backgrounds, and limitations in acquiring global …
irregularly shaped cracks, complex image backgrounds, and limitations in acquiring global …
Deep clustering via center-oriented margin free-triplet loss for skin lesion detection in highly imbalanced datasets
Melanoma is a fatal skin cancer that is curable and has dramatically increasing survival rate
when diagnosed at early stages. Learning-based methods hold significant promise for the …
when diagnosed at early stages. Learning-based methods hold significant promise for the …
An efficient artificial rabbits optimization based on mutation strategy for skin cancer prediction
M Abd Elaziz, A Dahou, A Mabrouk… - Computers in Biology …, 2023 - Elsevier
Accurate skin lesion diagnosis is critical for the early detection of melanoma. However, the
existing approaches are unable to attain substantial levels of accuracy. Recently, pre-trained …
existing approaches are unable to attain substantial levels of accuracy. Recently, pre-trained …
Boundary guided semantic learning for real-time COVID-19 lung infection segmentation system
The coronavirus disease 2019 (COVID-19) continues to have a negative impact on
healthcare systems around the world, though the vaccines have been developed and …
healthcare systems around the world, though the vaccines have been developed and …
A novel vision transformer model for skin cancer classification
Skin cancer can be fatal if it is found to be malignant. Modern diagnosis of skin cancer
heavily relies on visual inspection through clinical screening, dermoscopy, or …
heavily relies on visual inspection through clinical screening, dermoscopy, or …
Transy-net: Learning fully transformer networks for change detection of remote sensing images
In the remote sensing field, change detection (CD) aims to identify and localize the changed
regions from dual-phase images over the same places. Recently, it has achieved great …
regions from dual-phase images over the same places. Recently, it has achieved great …
[HTML][HTML] Stimulus-guided adaptive transformer network for retinal blood vessel segmentation in fundus images
Automated retinal blood vessel segmentation in fundus images provides important evidence
to ophthalmologists in coping with prevalent ocular diseases in an efficient and non-invasive …
to ophthalmologists in coping with prevalent ocular diseases in an efficient and non-invasive …