Vision Transformers in medical computer vision—A contemplative retrospection
Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …
contained within images, have evolved as one of the most contemporary and dominant …
3D deep learning on medical images: a review
The rapid advancements in machine learning, graphics processing technologies and the
availability of medical imaging data have led to a rapid increase in the use of deep learning …
availability of medical imaging data have led to a rapid increase in the use of deep learning …
[HTML][HTML] An overview of deep learning in medical imaging focusing on MRI
AS Lundervold, A Lundervold - Zeitschrift für Medizinische Physik, 2019 - Elsevier
What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
Deep learning in medical imaging and radiation therapy
The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes
Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular
carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor …
carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor …
Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …
the health and well-being of millions of people worldwide. Structural and functional …
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 …
Rethinking dice loss for medical image segmentation
Deep learning has proved to be a powerful tool for medical image analysis in recent years.
Data imbalance is a common problem in medical images. Dice Loss is widely used in …
Data imbalance is a common problem in medical images. Dice Loss is widely used in …
SpineParseNet: spine parsing for volumetric MR image by a two-stage segmentation framework with semantic image representation
S Pang, C Pang, L Zhao, Y Chen, Z Su… - … on Medical Imaging, 2020 - ieeexplore.ieee.org
Spine parsing (ie, multi-class segmentation of vertebrae and intervertebral discs (IVDs)) for
volumetric magnetic resonance (MR) image plays a significant role in various spinal disease …
volumetric magnetic resonance (MR) image plays a significant role in various spinal disease …
Unpaired multi-modal segmentation via knowledge distillation
Multi-modal learning is typically performed with network architectures containing modality-
specific layers and shared layers, utilizing co-registered images of different modalities. We …
specific layers and shared layers, utilizing co-registered images of different modalities. We …