Evaluating synthetic medical images using artificial intelligence with the GAN algorithm
In recent years, considerable work has been conducted on the development of synthetic
medical images, but there are no satisfactory methods for evaluating their medical suitability …
medical images, but there are no satisfactory methods for evaluating their medical suitability …
Make-a-volume: Leveraging latent diffusion models for cross-modality 3d brain mri synthesis
Cross-modality medical image synthesis is a critical topic and has the potential to facilitate
numerous applications in the medical imaging field. Despite recent successes in deep …
numerous applications in the medical imaging field. Despite recent successes in deep …
The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review
Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
How artificial intelligence is shaping medical imaging technology: A survey of innovations and applications
L Pinto-Coelho - Bioengineering, 2023 - mdpi.com
The integration of artificial intelligence (AI) into medical imaging has guided in an era of
transformation in healthcare. This literature review explores the latest innovations and …
transformation in healthcare. This literature review explores the latest innovations and …
CBCT-guided adaptive radiotherapy using self-supervised sequential domain adaptation with uncertainty estimation
Adaptive radiotherapy (ART) is an advanced technology in modern cancer treatment that
incorporates progressive changes in patient anatomy into active plan/dose adaption during …
incorporates progressive changes in patient anatomy into active plan/dose adaption during …
AIGAN: Attention–encoding Integrated Generative Adversarial Network for the reconstruction of low-dose CT and low-dose PET images
X-ray computed tomography (CT) and positron emission tomography (PET) are two of the
most commonly used medical imaging technologies for the evaluation of many diseases …
most commonly used medical imaging technologies for the evaluation of many diseases …
Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks
In radiation oncology, predicting patient risk stratification allows specialization of therapy
intensification as well as selecting between systemic and regional treatments, all of which …
intensification as well as selecting between systemic and regional treatments, all of which …
A generalized dual-domain generative framework with hierarchical consistency for medical image reconstruction and synthesis
Medical image reconstruction and synthesis are critical for imaging quality, disease
diagnosis and treatment. Most of the existing generative models ignore the fact that medical …
diagnosis and treatment. Most of the existing generative models ignore the fact that medical …
Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose CT denoising
F Zhao, M Liu, Z Gao, X Jiang, R Wang… - Computers in Biology and …, 2023 - Elsevier
Removing the noise in low-dose CT (LDCT) is crucial to improving the diagnostic quality.
Previously, many supervised or unsupervised deep learning-based LDCT denoising …
Previously, many supervised or unsupervised deep learning-based LDCT denoising …
Deep learning‐based convolutional neural network for intramodality brain MRI synthesis
Purpose The existence of multicontrast magnetic resonance (MR) images increases the
level of clinical information available for the diagnosis and treatment of brain cancer …
level of clinical information available for the diagnosis and treatment of brain cancer …