AIMC Topic: Image Interpretation, Computer-Assisted

Clear Filters Showing 141 to 150 of 3033 articles

Are Diffusion Models Effective Good Feature Extractors for MRI Discriminative Tasks?

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Diffusion models (DMs) excel in pixel-level and spatial tasks and are proven feature extractors for 2D image discriminative tasks when pretrained. However, their capabilities in 3D MRI discriminative tasks remain largely untapped. This study...

Development and validation of a collaborative framework for assessment of peripheral facial paralysis using facial image regions of interest.

Acta oto-laryngologica
BACKGROUND: While accurate evaluation of PFP is crucial for determining optimal treatment strategies, current clinical assessments rely heavily on subjective evaluations, leading to considerable variability between inter- and intra-observer ratings.

Deep learning segmentation of periarterial and perivenous capillary-free zones in optical coherence tomography angiography.

Journal of biomedical optics
SIGNIFICANCE: Automated segmentation of periarterial and perivenous capillary-free zones (CFZs) in optical coherence tomography angiography (OCTA) can significantly improve early detection and monitoring of diabetic retinopathy (DR), a leading cause ...

Speckle pattern analysis with deep learning for low-cost stroke detection: a phantom-based feasibility study.

Journal of biomedical optics
SIGNIFICANCE: Stroke is a leading cause of disability worldwide, necessitating rapid and accurate diagnosis to limit irreversible brain damage. However, many advanced imaging modalities (computerized tomography, magnetic resonance imaging) remain ina...

Unsupervised Test-Time Adaptation for Hepatic Steatosis Grading Using Ultrasound B-Mode Images.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound (US) is considered a key modality for the clinical assessment of hepatic steatosis (i.e., fatty liver) due to its noninvasiveness and availability. Deep learning methods have attracted considerable interest in this field, as they are capab...

MedFILIP: Medical Fine-Grained Language-Image Pre-Training.

IEEE journal of biomedical and health informatics
Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to accurately characterize associations between images and diseases, leading...

Supervised Information Mining From Weakly Paired Images for Breast IHC Virtual Staining.

IEEE transactions on medical imaging
Immunohistochemistry (IHC) examination is essential to determine the tumour subtypes, provide key prognostic factors, and develop personalized treatment plans for breast cancer. However, compared to Hematoxylin and Eosin (H&E) staining, the preparati...

Progressive Distillation With Optimal Transport for Federated Incomplete Multi-Modal Learning of Brain Tumor Segmentation.

IEEE journal of biomedical and health informatics
Multi-modal Magnetic Resonance Imaging (MRI) provide sufficient complementary information for brain tumor segmentation, however, most current approaches rely on complete modalities and may collapse with incomplete modalities. Moreover, most existing ...

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma From Multi-Sequence Magnetic Resonance Imaging Based on Deep Fusion Representation Learning.

IEEE journal of biomedical and health informatics
Recent studies have identified microvascular invasion (MVI) as the most vital independent biomarker associated with early tumor recurrence. With advancements in medical technology, several computational methods have been developed to predict preopera...

A Lesion-Fusion Neural Network for Multi-View Diabetic Retinopathy Grading.

IEEE journal of biomedical and health informatics
As the most common complication of diabetes, diabetic retinopathy (DR) is one of the main causes of irreversible blindness. Automatic DR grading plays a crucial role in early diagnosis and intervention, reducing the risk of vision loss in people with...