AIMC Topic:
Image Interpretation, Computer-Assisted
Clear Filters Showing 1621 to 1630 of 2731 articles
Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.
The Journal of pathology
Sep 3, 2019
In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated pa...
Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis.
IEEE transactions on cybernetics
Sep 2, 2019
Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients. So far, computer-aided diagnosis has not been widely applied in pa...
Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study.
Scientific reports
Aug 29, 2019
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
Aug 28, 2019
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
Aug 27, 2019
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
Aug 24, 2019
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
Aug 24, 2019
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
Aug 22, 2019
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
Aug 22, 2019
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
Aug 20, 2019
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...
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