U-net and its variants for medical image segmentation: A review of theory and applications
U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …
tasks. These traits provide U-net with a high utility within the medical imaging community …
Medical image segmentation using deep learning: A survey
Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …
papers has been presented recording the success of deep learning in the field. A …
Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends
Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
Deep semantic segmentation of natural and medical images: a review
The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …
instance, where each instance corresponds to a class. This task is a part of the concept of …
Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images
History shows that the infectious disease (COVID-19) can stun the world quickly, causing
massive losses to health, resulting in a profound impact on the lives of billions of people …
massive losses to health, resulting in a profound impact on the lives of billions of people …
Ev-eye: Rethinking high-frequency eye tracking through the lenses of event cameras
G Zhao, Y Yang, J Liu, N Chen… - Advances in Neural …, 2023 - proceedings.neurips.cc
In this paper, we present EV-Eye, a first-of-its-kind large scale multimodal eye tracking
dataset aimed at inspiring research on high-frequency eye/gaze tracking. EV-Eye utilizes an …
dataset aimed at inspiring research on high-frequency eye/gaze tracking. EV-Eye utilizes an …
Towards complete and accurate iris segmentation using deep multi-task attention network for non-cooperative iris recognition
Iris images captured in non-cooperative environments often suffer from adverse noise, which
challenges many existing iris segmentation methods. To address this problem, this paper …
challenges many existing iris segmentation methods. To address this problem, this paper …
[HTML][HTML] A survey of identity recognition via data fusion and feature learning
With the rapid development of the Mobile Internet and the Industrial Internet of Things, a
variety of applications put forward an urgent demand for user and device identity …
variety of applications put forward an urgent demand for user and device identity …
Fast and efficient retinal blood vessel segmentation method based on deep learning network
The segmentation of the retinal vascular tree presents a major step for detecting ocular
pathologies. The clinical context expects higher segmentation performance with a reduced …
pathologies. The clinical context expects higher segmentation performance with a reduced …
Attention guided U-Net with atrous convolution for accurate retinal vessels segmentation
Y Lv, H Ma, J Li, S Liu - IEEE Access, 2020 - ieeexplore.ieee.org
The accuracy of retinal vessels segmentation is of great significance for the diagnosis of
cardiovascular diseases such as diabetes and hypertension. Especially, the segmentation …
cardiovascular diseases such as diabetes and hypertension. Especially, the segmentation …