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 …
Review of medical image processing using quantum-enabled algorithms
Efficient and reliable storage, analysis, and transmission of medical images are imperative
for accurate diagnosis, treatment, and management of various diseases. Since quantum …
for accurate diagnosis, treatment, and management of various diseases. Since quantum …
Hard sample aware network for contrastive deep graph clustering
Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via
contrastive mechanisms, is a challenging research spot. Among the recent works, hard …
contrastive mechanisms, is a challenging research spot. Among the recent works, hard …
[HTML][HTML] DCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation
Deep learning architecture with convolutional neural network achieves outstanding success
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …
Ma-net: A multi-scale attention network for liver and tumor segmentation
T Fan, G Wang, Y Li, H Wang - IEEE Access, 2020 - ieeexplore.ieee.org
Automatic assessing the location and extent of liver and liver tumor is critical for radiologists,
diagnosis and the clinical process. In recent years, a large number of variants of U-Net …
diagnosis and the clinical process. In recent years, a large number of variants of U-Net …
Cluster-guided contrastive graph clustering network
Benefiting from the intrinsic supervision information exploitation capability, contrastive
learning has achieved promising performance in the field of deep graph clustering recently …
learning has achieved promising performance in the field of deep graph clustering recently …
Connected-UNets: a deep learning architecture for breast mass segmentation
Breast cancer analysis implies that radiologists inspect mammograms to detect suspicious
breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic …
breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic …
Image segmentation evaluation: a survey of methods
Z Wang, E Wang, Y Zhu - Artificial Intelligence Review, 2020 - Springer
Image segmentation is a prerequisite for image processing. There are many methods for
image segmentation, and as a result, a great number of methods for evaluating …
image segmentation, and as a result, a great number of methods for evaluating …
Collaborative unsupervised domain adaptation for medical image diagnosis
Deep learning based medical image diagnosis has shown great potential in clinical
medicine. However, it often suffers two major difficulties in real-world applications: 1) only …
medicine. However, it often suffers two major difficulties in real-world applications: 1) only …
[HTML][HTML] Segmentation and classification of brain tumor using 3D-UNet deep neural networks
P Agrawal, N Katal, N Hooda - International Journal of Cognitive …, 2022 - Elsevier
Early detection and diagnosis of a brain tumor enhance the medical options and the
patient's chance of recovery. Magnetic resonance imaging (MRI) is used to detect and …
patient's chance of recovery. Magnetic resonance imaging (MRI) is used to detect and …