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 review of medical image data augmentation techniques for deep learning applications
Research in artificial intelligence for radiology and radiotherapy has recently become
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
A review of deep-learning-based medical image segmentation methods
As an emerging biomedical image processing technology, medical image segmentation has
made great contributions to sustainable medical care. Now it has become an important …
made great contributions to sustainable medical care. Now it has become an important …
[HTML][HTML] Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation
Automatic segmentation methods are an important advancement in medical image analysis.
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
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 …
Modality specific U-Net variants for biomedical image segmentation: a survey
With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …
neural network, residual neural network, adversarial network; U-Net architectures are most …
MS-Net: multi-site network for improving prostate segmentation with heterogeneous MRI data
Automated prostate segmentation in MRI is highly demanded for computer-assisted
diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress …
diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress …
MADGAN: Unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction
Background Unsupervised learning can discover various unseen abnormalities, relying on
large-scale unannotated medical images of healthy subjects. Towards this, unsupervised …
large-scale unannotated medical images of healthy subjects. Towards this, unsupervised …
[HTML][HTML] Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy
Background Colonoscopy remains the gold-standard screening for colorectal cancer.
However, significant miss rates for polyps have been reported, particularly when there are …
However, significant miss rates for polyps have been reported, particularly when there are …
[HTML][HTML] End-to-end prostate cancer detection in bpMRI via 3D CNNs: effects of attention mechanisms, clinical priori and decoupled false positive reduction
We present a multi-stage 3D computer-aided detection and diagnosis (CAD) model 1 for
automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR …
automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR …