Deep Learning-powered biomedical photoacoustic imaging
Photoacoustic Imaging (PAI) is an emerging hybrid imaging modality that combines optical
imaging and ultrasound imaging, offering advantages such as high resolution, strong …
imaging and ultrasound imaging, offering advantages such as high resolution, strong …
Review of robot-assisted medical ultrasound imaging systems: Technology and clinical applications
Abstract Robot-assisted Medical Ultrasound Imaging Systems (RMUIS) leverage the
accuracy and stability of robotic motion and offer the potential to standardize medical …
accuracy and stability of robotic motion and offer the potential to standardize medical …
[HTML][HTML] Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound …
Despite a worldwide decline in maternal mortality over the past two decades, a significant
gap persists between low-and high-income countries, with 94% of maternal mortality …
gap persists between low-and high-income countries, with 94% of maternal mortality …
LM-Net: A light-weight and multi-scale network for medical image segmentation
Current medical image segmentation approaches have limitations in deeply exploring multi-
scale information and effectively combining local detail textures with global contextual …
scale information and effectively combining local detail textures with global contextual …
Fully automated thyroid ultrasound screening utilizing multi-modality image and anatomical prior
J Zhou, H Tian, W Wang - Biomedical Signal Processing and Control, 2024 - Elsevier
There is a high prevalence of thyroid nodules in the general population. Early detection is
essential for the treatment of malignant thyroid nodules. Ultrasound has the advantage of …
essential for the treatment of malignant thyroid nodules. Ultrasound has the advantage of …
Fully automated diagnosis of thyroid nodule ultrasound using brain-inspired inference
The interpretability of artificial intelligence (AI) based medical diagnostic systems is crucial to
make the diagnosis adequately convincible. Deep learning has been extensively …
make the diagnosis adequately convincible. Deep learning has been extensively …
SIB-UNet: A dual encoder medical image segmentation model with selective fusion and information bottleneck fusion
G Li, M Qi - Expert Systems with Applications, 2024 - Elsevier
Medical image segmentation aims to accurately mark the lesion area in the image to assist
doctors in disease diagnosis and guidance of surgical operations. However, the shape and …
doctors in disease diagnosis and guidance of surgical operations. However, the shape and …
Fetal cardiac ultrasound standard section detection model based on multitask learning and mixed attention mechanism
J He, L Yang, B Liang, S Li, C Xu - Neurocomputing, 2024 - Elsevier
Fetal cardiac ultrasound is a valuable tool for screening fetal cardiac health during
pregnancy. Ultrasound standard section testing is an essential part of fetal heart ultrasound …
pregnancy. Ultrasound standard section testing is an essential part of fetal heart ultrasound …
Segmentation-assisted hierarchical constrained state space approach for robust carotid artery wall motion measurement
The common carotid artery (CCA) wall motion measurement has great clinical significance
for preventing the progression of subclinical cardiovascular diseases. However, it is …
for preventing the progression of subclinical cardiovascular diseases. However, it is …
Domain composition and attention network trained with synthesized unlabeled images for generalizable medical image segmentation
Despite that deep learning models have achieved remarkable performance in medical
image segmentation, their performance is often limited on testing images from new centers …
image segmentation, their performance is often limited on testing images from new centers …