A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation
Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with
applications in disease diagnostics, intervention, and treatment planning. Accurate MRI …
applications in disease diagnostics, intervention, and treatment planning. Accurate MRI …
Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration
L Wang, Q Yang, C Wang, W Wang, J Pan… - arXiv preprint arXiv …, 2023 - arxiv.org
Recent years have witnessed the remarkable performance of diffusion models in various
vision tasks. However, for image restoration that aims to recover clear images with sharper …
vision tasks. However, for image restoration that aims to recover clear images with sharper …
[HTML][HTML] 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 …
EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model
Large-scale, big-variant, and high-quality data are crucial for developing robust and
successful deep-learning models for medical applications since they potentially enable …
successful deep-learning models for medical applications since they potentially enable …
A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Models
Denoising diffusion models have found applications in image segmentation by generating
segmented masks conditioned on images. Existing studies predominantly focus on adjusting …
segmented masks conditioned on images. Existing studies predominantly focus on adjusting …
DBEF-Net: Diffusion-Based Boundary-Enhanced Fusion Network for medical image segmentation
Z Huang, J Li, N Mao, G Yuan, J Li - Expert Systems with Applications, 2024 - Elsevier
Medical image segmentation aims to locate lesions within a given image to assist doctors in
diagnosis and treatment, playing a crucial role in improving patient outcomes. Recently, the …
diagnosis and treatment, playing a crucial role in improving patient outcomes. Recently, the …
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction
Q Liu, E Fuster-Garcia, IT Hovden… - arXiv preprint arXiv …, 2023 - arxiv.org
Diffuse gliomas are malignant brain tumors that grow widespread through the brain. The
complex interactions between neoplastic cells and normal tissue, as well as the treatment …
complex interactions between neoplastic cells and normal tissue, as well as the treatment …
Probabilistic Brain Extraction in MR Images via Conditional Generative Adversarial Networks
Brain extraction, or the task of segmenting the brain in MR images, forms an essential step
for many neuroimaging applications. These include quantifying brain tissue volumes …
for many neuroimaging applications. These include quantifying brain tissue volumes …
A multi-attention and depthwise separable convolution network for medical image segmentation
Automatic medical image segmentation method is highly needed to help experts in lesion
segmentation. The deep learning technology emerging has profoundly driven the …
segmentation. The deep learning technology emerging has profoundly driven the …
MSEF-Net: Multi-scale edge fusion network for lumbosacral plexus segmentation with MR image
Nerve damage of spine areas is a common cause of disability and paralysis. The
lumbosacral plexus segmentation from magnetic resonance imaging (MRI) scans plays an …
lumbosacral plexus segmentation from magnetic resonance imaging (MRI) scans plays an …