A survey on active learning and human-in-the-loop deep learning for medical image analysis
Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically …
including image acquisition, analysis and interpretation, and for the extraction of clinically …
[HTML][HTML] Deep learning based synthesis of MRI, CT and PET: Review and analysis
Medical image synthesis represents a critical area of research in clinical decision-making,
aiming to overcome the challenges associated with acquiring multiple image modalities for …
aiming to overcome the challenges associated with acquiring multiple image modalities for …
ResViT: residual vision transformers for multimodal medical image synthesis
Generative adversarial models with convolutional neural network (CNN) backbones have
recently been established as state-of-the-art in numerous medical image synthesis tasks …
recently been established as state-of-the-art in numerous medical image synthesis tasks …
Unsupervised medical image translation with adversarial diffusion models
Imputation of missing images via source-to-target modality translation can improve diversity
in medical imaging protocols. A pervasive approach for synthesizing target images involves …
in medical imaging protocols. A pervasive approach for synthesizing target images involves …
On data augmentation for GAN training
Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance
of using more data in GAN training. Yet it is expensive to collect data in many domains such …
of using more data in GAN training. Yet it is expensive to collect data in many domains such …
Hi-net: hybrid-fusion network for multi-modal MR image synthesis
Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …
[HTML][HTML] Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
Generative AI for brain image computing and brain network computing: a review
Recent years have witnessed a significant advancement in brain imaging techniques that
offer a non-invasive approach to map** the structure and function of the brain …
offer a non-invasive approach to map** the structure and function of the brain …
Brain tumor diagnosis using machine learning, convolutional neural networks, capsule neural networks and vision transformers, applied to MRI: a survey
Management of brain tumors is based on clinical and radiological information with
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …
Expanding small-scale datasets with guided imagination
The power of DNNs relies heavily on the quantity and quality of training data. However,
collecting and annotating data on a large scale is often expensive and time-consuming. To …
collecting and annotating data on a large scale is often expensive and time-consuming. To …