A review on medical imaging synthesis using deep learning and its clinical applications
This paper reviewed the deep learning‐based studies for medical imaging synthesis and its
clinical application. Specifically, we summarized the recent developments of deep learning …
clinical application. Specifically, we summarized the recent developments of deep learning …
Generative adversarial network in medical imaging: A review
Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …
community due to their capability of data generation without explicitly modelling the …
Class-aware adversarial transformers for medical image segmentation
Transformers have made remarkable progress towards modeling long-range dependencies
within the medical image analysis domain. However, current transformer-based models …
within the medical image analysis domain. However, current transformer-based models …
CT super-resolution GAN constrained by the identical, residual, and cycle learning ensemble (GAN-CIRCLE)
In this paper, we present a semi-supervised deep learning approach to accurately recover
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …
Recurrent mask refinement for few-shot medical image segmentation
Although having achieved great success in medical image segmentation, deep
convolutional neural networks usually require a large dataset with manual annotations for …
convolutional neural networks usually require a large dataset with manual annotations for …
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
Commercial iterative reconstruction techniques help to reduce the radiation dose of
computed tomography (CT), but altered image appearance and artefacts can limit their …
computed tomography (CT), but altered image appearance and artefacts can limit their …
SACNN: Self-attention convolutional neural network for low-dose CT denoising with self-supervised perceptual loss network
Computed tomography (CT) is a widely used screening and diagnostic tool that allows
clinicians to obtain a high-resolution, volumetric image of internal structures in a non …
clinicians to obtain a high-resolution, volumetric image of internal structures in a non …
Medical image generation using generative adversarial networks: A review
Generative adversarial networks (GANs) are unsupervised deep learning approach in the
computer vision community which has gained significant attention from the last few years in …
computer vision community which has gained significant attention from the last few years in …
Bidirectional mapping generative adversarial networks for brain MR to PET synthesis
Fusing multi-modality medical images, such as magnetic resonance (MR) imaging and
positron emission tomography (PET), can provide various anatomical and functional …
positron emission tomography (PET), can provide various anatomical and functional …
Creating artificial images for radiology applications using generative adversarial networks (GANs)–a systematic review
Rationale and Objectives Generative adversarial networks (GANs) are deep learning
models aimed at generating fake realistic looking images. These novel models made a great …
models aimed at generating fake realistic looking images. These novel models made a great …