Image inpainting based on deep learning: A review
Z Qin, Q Zeng, Y Zong, F Xu - Displays, 2021 - Elsevier
Image inpainting aims to restore the pixel features of damaged parts in incomplete image
and plays a key role in many computer vision tasks. Image inpainting technology based on …
and plays a key role in many computer vision tasks. Image inpainting technology based on …
Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review
Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract
features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests …
features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests …
Monai: An open-source framework for deep learning in healthcare
Artificial Intelligence (AI) is having a tremendous impact across most areas of science.
Applications of AI in healthcare have the potential to improve our ability to detect, diagnose …
Applications of AI in healthcare have the potential to improve our ability to detect, diagnose …
Vectorfusion: Text-to-svg by abstracting pixel-based diffusion models
Diffusion models have shown impressive results in text-to-image synthesis. Using massive
datasets of captioned images, diffusion models learn to generate raster images of highly …
datasets of captioned images, diffusion models learn to generate raster images of highly …
[HTML][HTML] Deep learning for generic object detection: A survey
Object detection, one of the most fundamental and challenging problems in computer vision,
seeks to locate object instances from a large number of predefined categories in natural …
seeks to locate object instances from a large number of predefined categories in natural …
Fss-1000: A 1000-class dataset for few-shot segmentation
Over the past few years, we have witnessed the success of deep learning in image
recognition thanks to the availability of large-scale human-annotated datasets such as …
recognition thanks to the availability of large-scale human-annotated datasets such as …
DeepIGeoS: a deep interactive geodesic framework for medical image segmentation
Accurate medical image segmentation is essential for diagnosis, surgical planning and
many other applications. Convolutional Neural Networks (CNNs) have become the state-of …
many other applications. Convolutional Neural Networks (CNNs) have become the state-of …
Nested network with two-stream pyramid for salient object detection in optical remote sensing images
Arising from the various object types and scales, diverse imaging orientations, and cluttered
backgrounds in optical remote sensing image (RSI), it is difficult to directly extend the …
backgrounds in optical remote sensing image (RSI), it is difficult to directly extend the …
Nuclei segmentation with recurrent residual convolutional neural networks based U-Net (R2U-Net)
Bio-medical image segmentation is one of the promising sectors where nuclei segmentation
from high-resolution histopathological images enables extraction of very high-quality …
from high-resolution histopathological images enables extraction of very high-quality …
[HTML][HTML] COVID TV-Unet: Segmenting COVID-19 chest CT images using connectivity imposed Unet
The novel corona-virus disease (COVID-19) pandemic has caused a major outbreak in more
than 200 countries around the world, leading to a severe impact on the health and life of …
than 200 countries around the world, leading to a severe impact on the health and life of …