AIMC Topic: Image Interpretation, Computer-Assisted

Clear Filters Showing 1831 to 1840 of 3033 articles

Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study.

Scientific reports
Medical images such as magnetic resonance (MR) imaging provide valuable information for cancer detection, diagnosis, and prognosis. In addition to the anatomical information these images provide, machine learning can identify texture features from th...

Development of accurate human head models for personalized electromagnetic dosimetry using deep learning.

NeuroImage
The development of personalized human head models from medical images has become an important topic in the electromagnetic dosimetry field, including the optimization of electrostimulation, safety assessments, etc. Human head models are commonly gene...

Connectomic Profiling Identifies Responders to Vagus Nerve Stimulation.

Annals of neurology
OBJECTIVE: Vagus nerve stimulation (VNS) is a common treatment for medically intractable epilepsy, but response rates are highly variable, with no preoperative means of identifying good candidates. This study aimed to predict VNS response using struc...

Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis.

Medical image analysis
Multiple sclerosis (MS) is the most common demyelinating disease. In MS, demyelination occurs in the white matter of the brain and in the spinal cord. It is thus essential to measure the tissue myelin content to understand the physiopathology of MS, ...

Identifying schizophrenia subgroups using clustering and supervised learning.

Schizophrenia research
Schizophrenia has a 1% incidence rate world-wide and those diagnosed present with positive (e.g. hallucinations, delusions), negative (e.g. apathy, asociality), and cognitive symptoms. However, both symptom burden and associated brain alterations are...

Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics.

Journal of medical imaging and radiation sciences
Progress in computing power and advances in medical imaging over recent decades have culminated in new opportunities for artificial intelligence (AI), computer vision, and using radiomics to facilitate clinical decision-making. These opportunities ar...

Toward an interpretable Alzheimer's disease diagnostic model with regional abnormality representation via deep learning.

NeuroImage
In this paper, we propose a novel method for magnetic resonance imaging based Alzheimer's disease (AD) or mild cognitive impairment (MCI) diagnosis that systematically integrates voxel-based, region-based, and patch-based approaches into a unified fr...

Deeply-Supervised Networks With Threshold Loss for Cancer Detection in Automated Breast Ultrasound.

IEEE transactions on medical imaging
ABUS, or Automated breast ultrasound, is an innovative and promising method of screening for breast examination. Comparing to common B-mode 2D ultrasound, ABUS attains operator-independent image acquisition and also provides 3D views of the whole bre...

Localizing B-Lines in Lung Ultrasonography by Weakly Supervised Deep Learning, In-Vivo Results.

IEEE journal of biomedical and health informatics
Lung ultrasound (LUS) is nowadays gaining growing attention from both the clinical and technical world. Of particular interest are several imaging-artifacts, e.g., A- and B- line artifacts. While A-lines are a visual pattern which essentially represe...

Encoder-decoder with dense dilated spatial pyramid pooling for prostate MR images segmentation.

Computer assisted surgery (Abingdon, England)
Automatic segmentation of prostate magnetic resonance (MR) images has great significance for the diagnosis and clinical application of prostate diseases. It faces enormous challenges because of the low contrast of the tissue boundary and the small ef...