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 …
A survey on U-shaped networks in medical image segmentations
The U-shaped network is one of the end-to-end convolutional neural networks (CNNs). In
electron microscope segmentation of ISBI challenge 2012, the concise architecture and …
electron microscope segmentation of ISBI challenge 2012, the concise architecture and …
Dense nested attention network for infrared small target detection
Single-frame infrared small target (SIRST) detection aims at separating small targets from
clutter backgrounds. With the advances of deep learning, CNN-based methods have yielded …
clutter backgrounds. With the advances of deep learning, CNN-based methods have yielded …
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 …
Ms RED: A novel multi-scale residual encoding and decoding network for skin lesion segmentation
Abstract Computer-Aided Diagnosis (CAD) for dermatological diseases offers one of the
most notable showcases where deep learning technologies display their impressive …
most notable showcases where deep learning technologies display their impressive …
Improving nowcasting of convective development by incorporating polarimetric radar variables into a deep‐learning model
Nowcasting of convective storms is urgently needed yet rather challenging. Current
nowcasting methods are mostly based on radar echo extrapolation, which suffer from the …
nowcasting methods are mostly based on radar echo extrapolation, which suffer from the …
TransDose: Transformer-based radiotherapy dose prediction from CT images guided by super-pixel-level GCN classification
Radiotherapy is a mainstay treatment for cancer in clinic. An excellent radiotherapy
treatment plan is always based on a high-quality dose distribution map which is produced by …
treatment plan is always based on a high-quality dose distribution map which is produced by …
IVD-Net: Intervertebral disc localization and segmentation in MRI with a multi-modal UNet
Accurate localization and segmentation of intervertebral disc (IVD) is crucial for the
assessment of spine disease diagnosis. Despite the technological advances in medical …
assessment of spine disease diagnosis. Despite the technological advances in medical …
Multi-receptive-field CNN for semantic segmentation of medical images
The context-based convolutional neural network (CNN) is one of the most well-known CNNs
to improve the performance of semantic segmentation. It has achieved remarkable success …
to improve the performance of semantic segmentation. It has achieved remarkable success …
Neuroimaging and deep learning for brain stroke detection-A review of recent advancements and future prospects
Background and objective In recent years, deep learning algorithms have created a massive
impact on addressing research challenges in different domains. The medical field also …
impact on addressing research challenges in different domains. The medical field also …