[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 …
Learning unified hyper-network for multi-modal MR image synthesis and tumor segmentation with missing modalities
Accurate segmentation of brain tumors is of critical importance in clinical assessment and
treatment planning, which requires multiple MR modalities providing complementary …
treatment planning, which requires multiple MR modalities providing complementary …
Med-cDiff: Conditional medical image generation with diffusion models
Conditional image generation plays a vital role in medical image analysis as it is effective in
tasks such as super-resolution, denoising, and inpainting, among others. Diffusion models …
tasks such as super-resolution, denoising, and inpainting, among others. Diffusion models …
Ss-3dcapsnet: Self-supervised 3d capsule networks for medical segmentation on less labeled data
Capsule network is a recent new deep network architecture that has been applied
successfully for medical image segmentation tasks. This work extends capsule networks for …
successfully for medical image segmentation tasks. This work extends capsule networks for …
A unified hyper-GAN model for unpaired multi-contrast MR image translation
Cross-contrast image translation is an important task for completing missing contrasts in
clinical diagnosis. However, most existing methods learn separate translator for each pair of …
clinical diagnosis. However, most existing methods learn separate translator for each pair of …
Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects
Abstract Machine learning (ML) applications in medical artificial intelligence (AI) systems
have shifted from traditional and statistical methods to increasing application of deep …
have shifted from traditional and statistical methods to increasing application of deep …
Diffusion-Based Approaches in Medical Image Generation and Analysis
Data scarcity in medical imaging poses significant challenges due to privacy concerns.
Diffusion models, a recent generative modeling technique, offer a potential solution by …
Diffusion models, a recent generative modeling technique, offer a potential solution by …
Multimodal Machine Learning for Clinically-Assistive Imaging-Based Biomedical Applications
E Warner, J Lee, W Hsu, T Syeda-Mahmood… - arXiv preprint arXiv …, 2023 - arxiv.org
Machine learning (ML) applications in medical artificial intelligence (AI) systems have shifted
from traditional and statistical methods to increasing application of deep learning models …
from traditional and statistical methods to increasing application of deep learning models …
PadGAN: An End-to-End dMRI Data Augmentation Method for Macaque Brain
Y Chen, L Zhang, X Xue, X Lu, H Li, Q Wang - Applied Sciences, 2024 - mdpi.com
Currently, an increasing number of macaque brain MRI datasets are being made publicly
accessible. Unlike human, publicly accessible macaque brain datasets suffer from data …
accessible. Unlike human, publicly accessible macaque brain datasets suffer from data …
Deep non-linear embedding deformation network for cross-modal brain mri synthesis
Multimodal MRI (eg T1, T2, and Flair) can provide rich anatomical and functional
information, thereby facilitating clinical diagnosis and treatment. However, multimodal MRI …
information, thereby facilitating clinical diagnosis and treatment. However, multimodal MRI …