Different multimodal data functional image processing computer assisted. Medical search. Frequent questions

EPViz also provides valuable tools for clinician-scientists, including spectrum visualization, computation of basic statistics, data anonymization, and annotation editing. (jhu.edu)
1994. " Real-Time Volume Visualization Of Ct And Nmr Images. " . (uni-heidelberg.de)
Studierfenster offers a wide range of capabilities, including the visualization of medical data (CT, MRI, etc.) in two-dimensional (2D) and three-dimensional (3D) space in common web browsers, such as Google Chrome, Mozilla Firefox, Safari, or Microsoft Edge. (springer.com)
Other functionalities are the calculation of medical metrics (dice score and Hausdorff distance), manual slice-by-slice outlining of structures in medical images, manual placing of (anatomical) landmarks in medical imaging data, visualization of medical data in virtual reality (VR), and a facial reconstruction and registration of medical data for augmented reality (AR). (springer.com)

Deep learning algorithms are also widely used in fMRI-assisted diagnosis of brain diseases. (hindawi.com)
I am proud to have initiated several new, multidisciplinary research projects that integrate imaging and non-imaging modalities, driving the development of innovative image analysis and machine learning algorithms. (ox.ac.uk)

the IEEE International Symposium on Biomedical Imaging (ISBI) 2023. (nottingham.ac.uk)

In this paper, a multimodal diagnosis method for AD based on three-dimensional shufflenet (3DShuffleNet) and principal component analysis network (PCANet) is proposed. (hindawi.com)
At present, structural MRI (sMRI) and functional MRI (fMRI) are widely used in the diagnosis of Alzheimer's disease (AD). (hindawi.com)
Meszlényi Regina [ 10 ] proposed a dynamic time normalization distance matrix, Pearson correlation coefficient matrix, warping path distance matrix, and convolutional neural network to realize AD-assisted diagnosis. (hindawi.com)
Medical image analysis is of tremendous importance in serving clinical diagnosis, treatment planning, as well as prognosis assessment. (frontiersin.org)
Medical imaging is widely employed in clinical research to investigate effects on diagnosis, staging, treatment planning, and follow-up evaluations ( 1 - 4 ). (frontiersin.org)
In the current and future video-capable and communication devices, the integration of assistive diagnosis applications based on computer vision is going to play an increasingly important role as it will be integrated in all types of Tele-Health strategies. (oulu.fi)
Technologies that increase the speed and accuracy of stroke diagnosis or assist in post-stroke rehabilitation can improve patient outcomes. (mdpi.com)
These models are restricted from performing certain tasks, like recognizing specific individuals in images or interpreting medical images for diagnosis, in order to ensure responsible and ethical use. (typethepipe.com)
Among his research works, those of significant importance include detecting abnormal patterns in complex visual and medical data, assisted diagnosis using automated image analysis, fully automated volumetric image segmentation, registration, and motion analysis, machine understanding of human action, efficient deep learning, and deep learning on irregular domains. (swansea.ac.uk)
Individuals with "access and functional needs" do not require any kind of diagnosis or specific evaluation. (cdc.gov)

Medical imaging contains multiple imaging sequences or modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), providing complementary information ( 5 - 8 ). (frontiersin.org)
Imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) are widely used in diagnostics, clinical studies, and treatment planning. (springer.com)
Since imaging modalities, such a computed tomography (CT) and magnetic resonance imaging (MRI), are widely used in diagnostics, clinical studies, and treatment planning, automatic algorithms for (bio-)medical image processing and analysis have become an invaluable tool in medicine. (springer.com)
Computed tomography (CT) is one of the most valuable diagnostic imaging tools in the clinic and is widely used worldwide. (sharif.edu)
This provides improved three-dimensional contrast and spatial resolution compared to conventional computed tomography , where data is obtained and computed from individual sequential exposures. (lookformedical.com)
Types of spiral computed tomography technology in which multiple slices of data are acquired simultaneously improving the resolution over single slice acquisition technology. (lookformedical.com)

MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS (to appear), 2021. (jhu.edu)
MIDL: Medical Imaging with Deep Learning, MLR:1-12, 2021. (jhu.edu)
2021) Planning brain tumor resection using a probabilistic atlas of cortical and subcortical structures critical for functional processing: a proof of concept, Operative Neurosurgery, 20(3), 175-183 https://doi.org/10.1093/ons/opaa396 -- -- Le informazioni contenute nella presente comunicazione sono di natura privata e come tali sono da considerarsi riservate ed indirizzate esclusivamente ai destinatari indicati e per le finalità strettamente legate al relativo contenuto. (python.org)
In 2018, he was awarded Rutherford Fund Fellowship at Health Data Research UK at the Big Data Institute in Oxford, which extended in Senior Fellowship in Population Health (from 2021). (ox.ac.uk)
Ramyad Hadidi received his Ph.D. in computer science from Georgia Institute of Technology in May 2021 under the supervision of Professor Hyesoon Kim with his thesis titled \"Deploying Deep Neural Networks in Edge with Distribution. (usc.edu)

Nevertheless, AI can likewise be effective throughout the diagnostic procedure in numerous methods, from offering insights that might help in reducing death rates and cause earlier detection and treatment choices, to assisting fix useful bandwidth constraints of clinicians throughout their workdays. (oktyabr76.ru)
Automated detection of COVID-19 through convolutional neural network using chest X-ray images. (springer.com)
Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. (springer.com)
More sophisticated features include the automatic cranial implant design with a convolutional neural network (CNN), the inpainting of aortic dissections with a generative adversarial network, and a CNN for automatic aortic landmark detection in CT angiography images. (springer.com)
It is obtained from combination of the reference-free damage detection technique and the probability-based diagnostic imaging method. (sharif.edu)
I am interested in innovating and applying Machine Learning techniques to various Computer Vision problems, including 3D reconstruction from single 2D images, 3D point cloud registration and classification, landmark detection of objects in 2D/3D images, semantic segmentation of 2D/3D images, object detection and classification in 2D images. (nottingham.ac.uk)
Cascaded Graph Convolution Approach for Nuclei Detection in Histopathology Images. (swansea.ac.uk)
A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods. (swansea.ac.uk)

Fan-like images due to modalities such as curved-array ultrasound are also common and require different representational and algorithmic techniques to process. (wikipedia.org)
Different quantitative features to evaluate LV dynamics known from other modalities (ultrasound, MR) are transferred to the C-arm CT data. (uni-erlangen.de)

Sayan Ghosal wins Best Student Paper in the Image Processing Conference of SPIE Medical Imaging! (jhu.edu)
Spie Medical Imaging , 5747:163-169. (uni-heidelberg.de)
Spie Medical Imaging. (uni-heidelberg.de)

The methods can be grouped into several broad categories: image segmentation, image registration, image-based physiological modeling, and others. (wikipedia.org)
Segmentation is the process of partitioning an image into different meaningful segments. (wikipedia.org)
Medical image segmentation is made difficult by low contrast, noise, and other imaging ambiguities. (wikipedia.org)
Although there are many computer vision techniques for image segmentation, some have been adapted specifically for medical image computing. (wikipedia.org)
Image-Based segmentation: Some methods initiate a template and refine its shape according to the image data while minimizing integral error measures, like the Active contour model and its variations. (wikipedia.org)
Manual segmentation, using tools such as a paint brush to explicitly define the tissue class of each pixel, remains the gold standard for many imaging applications. (wikipedia.org)
To segment an object, a segmentation seed is needed (that is the starting point that determines the approximate position of the object in the image). (wikipedia.org)
Convolutional neural networks (CNN's): The computer-assisted fully automated segmentation performance has been improved due to the advancement of machine learning models. (wikipedia.org)
Most recently, machine learning- and deep learning-based intelligent imaging analyses have shown enormous advantages in providing consistent and accurate image quantifications in multiple applications, including image segmentation, registration, classification, etc. ( 9 - 12 ). (frontiersin.org)
Notably, my research projects encompass both the theoretical foundations of AI/ML algorithms (such as image quality, image segmentation, or image registration), and applied AI/ML for longitudinal disease monitoring (using imaging, patient records, and Natural Language Processing), identification of disease therapeutic targets (using imaging & genetic data integration), and more recently, multimodal cancer imaging & radiogenomics. (ox.ac.uk)
Examples of this are two- and three-dimensional visualizations, image segmentation, and the registration of all anatomical structure and pathology types. (springer.com)
Segmentation, for example, is typically the first step in a (bio-)medical image analysis pipeline. (springer.com)
However, automatic medical image segmentation is known to be one of the most complex problems in image analysis and is still an object of active research. (springer.com)
Zhang already estimated in 2006 that there are over 4000 image segmentation algorithms [ 5 ] and this was well before the advent of the deep learning "era" [ 6 ]. (springer.com)

Longitudinal, time-varying acquisitions may or may not acquire images with regular time steps. (wikipedia.org)
The position is part of the "Neusurplan" project, an integrated approach to neurosurgery planning based on multimodal and longitudinal data. (python.org)
In this project, the candidate will pursue research on machine learning methods for neuroimaging data analysis to study and characterize brain connectivity, with applications to longitudinal studies and clinical practice. (python.org)

In addition, the PCANet network is applied to the brain function connection analysis, and the features on fMRI data are obtained. (hindawi.com)
Here we present Le Petit Prince fMRI Corpus (LPPC-fMRI) 5 , a multilingual fMRI dataset where English, Chinese and French speakers listened to the same audiobook Le Petit Prince (The Little Prince) in their native language (see Fig. 1 for a Schematic overview of the LPPC-fMRI data collection, preprocessing, technical validation and annotation procedures). (nature.com)
Schematic overview of the LPPC-fMRI data collection procedures, preprocessing, technical validation and annotation. (nature.com)

While closely related to the field of medical imaging, MIC focuses on the computational analysis of the images, not their acquisition. (wikipedia.org)
The brain structure imaging analysis of patients with AD and normal people (normal control, NC) has found that the gray matter volume of AD patients was significantly lower than that of normal people, and the gray matter in the hippocampus, temporal poles, and temporal islands also has significant shrinkage [ 1 ]. (hindawi.com)
However, the image analysis process usually involves multiple modality-specific software and relies on rigorous manual operations, which is time-consuming and potentially low reproducible. (frontiersin.org)
The processing and quantitative analysis of medical images ensure their clinical utility in a variety of medical applications, from general research to clinical workflows. (frontiersin.org)
The accurate analysis of medical images accelerates the development and upgrading of intelligent algorithms that can be integrated into the software to enable easy-to-use clinical research. (frontiersin.org)
Numerous choices of medical image analysis tools integrating advanced algorithms are available. (frontiersin.org)
Semi-supervised clustering of quaternion time series: application to gait analysis in multiple sclerosis using motion sensor data , Statistics in Medicine, accepted provisionally upon minor revisions . (github.io)
AI is currently playing a significant function in medical imaging by assisting to automate image analysis and decrease the quantity of time required to analyze scans. (oktyabr76.ru)
This annotated, multilingual fMRI dataset facilitates future re-analysis that addresses cross-linguistic commonalities and differences in the neural substrate of language processing on multiple perceptual and linguistic levels. (nature.com)
This Small Business Innovation Research Phase I project involves research on development and application of an optical data acquisition system, I/O devices, and optical data analysis and recognition. (nsf.gov)
Bartek joined the Biomedical Image Analysis Laboratory at the University of Oxford and between 2012 and 2017, he worked as a post-doctoral research fellow at the Oxford Cancer Imaging Centre focusing on cancer image analysis. (ox.ac.uk)
Bartek provides also lectures/workshop on Artificial Intelligence and Machine Learning (AI/ML) for Healthcare, Biomedical Image Analysis, Medical Imaging for various departments across the university and the external parties. (ox.ac.uk)
design of methodology for the different project phases including conception, data-set building and curation, ground-truth extraction strategy including labeling and AI-assisted annotation tools, exploratory data analysis, customization and implementation of deep learning architectures, model training, inference and clinical validation phase. (deephealth-project.eu)
Our research spans the development, implementation, and application of advanced optical instrumentation, as well as the acquisition, processing, and analysis of rich imaging datasets. (nih.gov)
how to relieve the problem of insufficient and imbalanced data for micro-gesture analysis. (oulu.fi)
Professor (Tenure Track) Miguel Bordallo Lopez adopts a multidisciplinary approach at the intersection of AI-assisted primary healthcare and real-time computer vision and signal analysis. (oulu.fi)
But, the application of deep learning in medical image analysis is limited by the scarcity of high-quality annotated medical imaging data. (springer.com)
Convolutional neural networks for medical image analysis: Full training or fine tuning? (springer.com)
Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. (springer.com)
Automatic algorithms for image analysis have thus become an invaluable tool in medicine. (springer.com)
a free, non-commercial open science client-server framework for (bio-)medical image analysis. (springer.com)
A user study with medical and non-medical experts in medical image analysis was performed, to evaluate the usability and the manual functionalities of Studierfenster. (springer.com)
In this contribution, we presented an online environment for (bio-)medical image analysis. (springer.com)
In the past few decades, image-based analysis of radiological datasets has gone through a remarkable period of rapid technological innovation. (springer.com)
In the theoretical aspect, currently I am focusing on Small Sample Size Problem in High Dimensional Data Modeling, a challenging problem that has recently drawn attention of researchers from various fields such as genomics, Image and Video Analysis, Chemometrics, Economics, and Humanities. (nottingham.ac.uk)
Note: Special Issue on Ultrasonic Image Processing and Analysis. (inria.fr)
Medical Image Analysis , 7(4):475-488, December 2003. (inria.fr)
Today, quantitative analysis of 3-D dynamics of the left ventricle (LV) cannot be performed directly in the catheter lab using a current angiographic C-arm system, which is the workhorse imaging modality for cardiac interventions. (uni-erlangen.de)
Therefore, myocardial wall analysis is completely based on the 2-D angiographic images or pre-interventional 3-D/4-D imaging. (uni-erlangen.de)
The proposed analysis tool was evaluated on simulated phantom LV data with and without pathological wall dysfunctions. (uni-erlangen.de)
We demonstrated that the combination light-path and private cloud is a viable means of building an analysis infrastructure for secure data analysis. (spie.org)
This paper presents a novel multimodal fusion algorithm - Trial Selection Tensor Canonical Correlation Analysis (TSTCCA) to optimize the feature space and build a more robust depression recognition model, which innovatively combines the spatiotemporal relevance and complementarity between facial expression and pupil diameter features. (bvsalud.org)
Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: A systematic review and meta-analysis. (cdc.gov)

At UTS, Prof. Piccardi serves as leader for the Big Data Analytics program of the Global Big Data Technologies Centre and as Head of Discipline, Signal Processing and Analytics, in the School of Electrical and Data Engineering. (deephealth-project.eu)
These companies are from various domains, such as robotics, intelligent systems and machines, IoT, health and big data analytics. (essex.ac.uk)
Reflecting on the PELARS project and Multimodal Learning Analytics. (upf.edu)
The aim is to share the results and challenges of the project to further the dialogue about how to increase progress for multimodal learning analytics (MMLA). (upf.edu)
Can "Big Data" analytics be a valuable asset from a regional management perspective? (spie.org)
This update features emerging roles of big data science, machine learning, and predictive analytics across the life span. (cdc.gov)

Breast cancer classification of image using convolutional neural network. (springer.com)
This study implemented a multi-modal image classification model that combines convolutional methods with natural language understanding of descriptions, titles, and tags to improve image classification. (bepress.com)

This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care. (wikipedia.org)
We present an integrated platform - uAI Research Portal (uRP), to achieve one-stop analyses of multimodal images such as CT, MRI, and PET for clinical research applications. (frontiersin.org)
With the continuous development and inclusion of advanced algorithms, we expect this platform to largely simplify the clinical scientific research process and promote more and better discoveries. (frontiersin.org)
She has a multidisciplinary expertise in artificial intelligence, natural language processing, software engineering, health technology, clinical oncology, clinical trials and cancer research industry. (deephealth-project.eu)
An example could be the processing of medical acquisitions like CT or MRI from animals [Clinical Pharmacology & Therapeutics, 84(4):448-456, 68 ], which get more and more common, as veterinary clinics and centers get more and more equipped with such imaging devices. (springer.com)
She completed a fellowship in clinical Movement Disorders under the mentorship of Dr. Stanley Fahn at Columbia University and post-doctoral training in Functional Neuroimaging with Dr. David Eidelberg at the Feinstein Institute. (stanford.edu)
Participants perceived that a multimodal online platform facilitated by clinical champions influences knowledge transfer, skills and behaviour, encourages workplace CPI activities. (biomedcentral.com)

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. (crossref.org)
Prof Piccardi serves as an Associate Editor for journal IEEE Transactions on Big Data and is a senior member of the IEEE, a member of the IEEE Computer and Systems, Man and Cybernetis Societies and a member of the International Association for Pattern Recognition. (deephealth-project.eu)

The last method (5) is an iterative few-view reconstruction (FV), the prior image constrained compressed sensing (PICCS) combined with the improved total variation (iTV) algorithm. (uni-erlangen.de)
Tomography using x-ray transmission and a computer algorithm to reconstruct the image. (lookformedical.com)

During data collection (blue), anatomical MRI was first acquired, followed by functional MRI while participants listened to 9 sections of the audiobook. (nature.com)

The goal is to pursue an integrated approach to pre-operative neurosurgical planning, combining structural and functional characterization of brain connectivity. (python.org)
Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. (biorxiv.org)

The particular meaning of the data at the sample point depends on modality: for example a CT acquisition collects radiodensity values, while an MRI acquisition may collect T1 or T2-weighted images. (wikipedia.org)
The Golby Lab pursues translational research multi-modality image-guided neurosurgery with a particular interest in functional brain mapping. (dana-farber.org)

Objective This study was conducted to evaluate the changes occurring in the thickness of deep trunk muscles, measured using ultrasound imaging, after 4 wks of lumbosacral orthosis use in conjunction with routine physical therapy. (sharif.edu)

In: Proceedings of the IEEE/CVF International Conference on Computer Vision. (crossref.org)
In Proceedings of the 33rd International Conference on Neural Information Processing Systems , Vancouver, Canada, Article number 301, 2019. (springer.com)
Proceedings of the International Symposium on Surgery Simulation and Soft Tissue Modeling , volume 2673 of Lecture Notes in Computer Science , Juan-les-Pins, France, June 2003. (inria.fr)

First, the data on structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are preprocessed to remove the influence resulting from the differences in image size and shape of different individuals, head movement, noise, and so on. (hindawi.com)
Magnetic resonance imaging (MRI) is a medical imaging technology with rapid development in recent years. (hindawi.com)
49 English speakers, 35 Chinese speakers and 28 French speakers listened to the same audiobook The Little Prince in their native language while multi-echo functional magnetic resonance imaging was acquired. (nature.com)
In this study, we are proposing a novel nonlinear classification approach to discriminate between Alzheimer's Disease (AD) and a control group using T1-weighted and T2-weighted Magnetic Resonance Images (MRI's) of brain. (sharif.edu)

The disadvantage is that, unlike positron-emission tomography where the positron-electron annihilation results in the emission of 2 photons at 180 degrees from each other, SPECT requires physical collimation to line up the photons, which results in the loss of many available photons and hence degrades the image. (lookformedical.com)

The ideal candidate should have a mixed background in neuroimaging techniques and numerate disciplines, like computer science, engineering, physics, or mathematics. (python.org)
In addition, I am always working on new neuroimaging, data processing, and modeling methods. (interdisciplinary-college.org)
At Dr. Poston's laboratory, Dr. Müller-Oehring is expanding her neuroimaging research to PET/MR aiming to identify biomarkers underwriting the functional status in Parkinson's disease. (stanford.edu)

The second part of my talk emphasizes the necessity of modern machine learning techniques, such as those utilizing heavy neural networks, in comprehending complex raw data in edge systems and acting upon the outcomes. (usc.edu)

The issues of big data in medical imaging informatics have special characters which need to be deal with in healthcare service and research. (spie.org)
One of the challenges of today's healthcare is that data from radiology is heterogeneous, stored and managed in silos created by PACS vendors. (spie.org)
Building trust by the use of Big Data in healthcare involves a long and winding journey, but the persevering infrastructure-oriented organization will give new ways of collaboration for the enterprise it serves. (spie.org)
Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data. (cdc.gov)

Multimodal learning and fusion have been deeply explored in the group. (oulu.fi)
When combined, multimodal fusion and autonomous learning can lead to more robust and efficient machine learning solutions in various fields and in forms of software, services, or smart products with emotional intelligence and self-learning towards 6G. (oulu.fi)
I believe fusion will be considered archaic, particularly cervical, because the data is so clear that ADR is superior to fusion in almost every outcome measure," said Dr. Lanman. (beckersspine.com)

To investigate the multimodal data, we started with a simple grading of the student's final products and progressed to a richer framework for assessing student non-verbal collaboration with different machine learning strategies. (upf.edu)
A secure private cloud computing framework facilitates interactive, computationally intensive exploration of this geographically distributed, privacy sensitive data. (spie.org)
The toolkit provides a framework to organize planning for broad groups of people with disabilities and others with access and functional needs, recommended action steps and noteworthy practices from the field. (cdc.gov)

1996. " Real-Time Direct Volume Rendering In Functional Imaging " . (uni-heidelberg.de)

We also provide time-aligned speech annotation and word-by-word predictors obtained using natural language processing tools. (nature.com)

These characters lead to many technical challenges in medical imaging informatics. (spie.org)
The possible and perspective solutions of big data issues in medical imaging informatics are discussed in this presentation, and also some of our research projects related to five V features of big data in medical imaging and informatics have been briefed. (spie.org)

2020) Mapping critical cortical hubs and white matter pathways by direct electrical stimulation: an original functional atlas of the human brain, Neuroimage, 205 https://doi.org/10.1016/j.neuroimage.2019.116237 Astolfi P, et al. (python.org)

His main research focus is fundamental research questions of artificial intelligence, machine learning and their applications in engineering, bioengineering, health informatics, human-robot interaction, computer vision, smart sensing & industrial informatics. (essex.ac.uk)

The objective, naturally, is to offer clinicians with faster outcomes, automate the analyses of client information, and assist them make much better notified choices about a client's care. (oktyabr76.ru)
Data-driven functional analyses provide further evidence of data quality. (nature.com)

The Golby Lab is a translational multidisciplinary group of investigators focusing on functional brain mapping using both structural and functional imaging techniques to guide neurosurgical planning and intra-operative decision making. (dana-farber.org)

Park HJ, Friston K. Structural and Functional Brain Networks: From Connections to Cognition. (interdisciplinary-college.org)

AI combined with machine learning methods is applied to medical image processing to obtain biomarkers and to assist doctors in making correct diagnoses. (hindawi.com)

Hence, incorporation of motion correction in the reconstruction step may improve the resulting image quality. (uni-erlangen.de)
This motion information can be used for a motion-compensated reconstruction allowing the use of all acquired data for image reconstruction. (uni-erlangen.de)
ECG-gating of data from a single C-arm rotation provides only a few projections per heart phase for image reconstruction. (uni-erlangen.de)
Five different initial image reconstructions are evaluated: (1) cone beam filtered-backprojection (FDK), (2) cone beam filtered-backprojection and an additional bilateral filter (FFDK), (3) removal of the shadow of dense objects (catheter, pacing electrode, etc.) before reconstruction with a cone beam filtered-backprojection (cathFDK), (4) removal of the shadow of dense objects before reconstruction with a cone beam filtered-backprojection and a bilateral filter (cathFFDK). (uni-erlangen.de)
When a catheter or pacing electrode is present, the shadow of these objects needs to be removed before the initial image reconstruction. (uni-erlangen.de)
This is repeated at various angles and a mathematical reconstruction provides three dimensional MEDICAL IMAGING of tissues. (lookformedical.com)

MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 12267:437-447, 2020. (jhu.edu)
2020) Tractogram filtering of anatomically non-plausible fibers with geometric deep learning, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) LNCS, vol 12267. (python.org)

Accordingly, NLP models can be leveraged to understand linguistic processes at an algorithmic level by comparing model predictions against brain data during naturalistic comprehension. (nature.com)

Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. (wikipedia.org)
Danielle Currey wins two Computer Science convocation awards! (jhu.edu)
Aurelia Bustos, MD, PhD. is board-certified medical oncologist practicing during more than 18 years and computer science engineer with a PhD in AI applied to medical text and medical image. (deephealth-project.eu)
Marco Danelutto , Full Professor at Dept. of Computer Science, Univ. (deephealth-project.eu)
Prof. Marco Danelutto obtained his PhD in Computer Science in 1990 and he is currently a full professor at the University of Pisa, Dept. of Computer Science. (deephealth-project.eu)
He has been teaching different courses related to parallel/distributed computing and he is currently vice-responsible for the master's degree in Computer science and Networking at the University of Pisa and vice-director of the Dept. of Computer Science of the Univ. (deephealth-project.eu)
She joined the School of Computer Science, University of Nottingham Malaysia Campus, as an Associate Professor in December 2012. (nottingham.ac.uk)
Professor Xianghua Xie is currently leading a research team on Computer Vision and Machine Learning (http://csvision.swan.ac.uk) in the Department of Computer Science, Swansea University. (swansea.ac.uk)
Prior to his position at Swansea, He was a Research Associate at the Computer Vision Group, Department of Computer Science, University of Bristol, where he completed both his PhD (2006) and MSc (2002) degrees. (swansea.ac.uk)
The Golby Lab is funded by the Brain Science Foundation, The Klarman Family Foundation, CIMIT Harvard Catalyst, and the National Institutes of Health, allowing her to assemble a team of extraordinary scientists from different backgrounds, working collaboratively to advance the field of image-guided surgery and functional brain imaging. (dana-farber.org)

CDC developed the Access and Functional Needs Toolkit: Integrating a Community Partner Network to Inform Risk Communication Strategies to help emergency planners achieve effective communications through the integration of a community outreach information network. (cdc.gov)

Atlas-based methods usually require the use of image registration in order to align the atlas image or images to a new, unseen image. (wikipedia.org)
2005. " Parameter Choice For Fast Rigid Multimodal Registration " . (uni-heidelberg.de)
Cardiac motion can be estimated by deformable 3-D/3-D registration performed on initial 3-D images of different heart phases. (uni-erlangen.de)
However, the result of the registration procedure and hence the estimated deformations are influenced by the quality of the initial 3-D images. (uni-erlangen.de)
In this paper, the sensitivity of the 3-D/3-D registration step to the image quality of the initial images is studied. (uni-erlangen.de)
The quantitative results of the phantom experiments show that if no dense object is present within the scan field of view, the quality of the FDK initial images is sufficient for motion estimation via 3-D/3-D registration. (uni-erlangen.de)

Its announcement in March surprised everyone, not only due to its improved intelligence but also its multimodal capabilities. (typethepipe.com)
OpenAI presented examples where GPT can take images as input, visualize, understand, analyze them, and use its text generation capabilities to reason and perform intelligent tasks. (typethepipe.com)
In March, the AI community was taken by storm when OpenAI unveiled GPT-4's Multimodal Capabilities. (typethepipe.com)
Behind OpenAI's advanced chatbot, ChatGPT, stands the formidable brain of GPT-4, an AI model celebrated for its exceptional intelligence and its remarkable multimodal capabilities. (typethepipe.com)

In addition, naturalistic approaches to neurolinguistics are in synergy with natural language processing (NLP), where using ecologically valid language corpora for training models has been common practice for the past quarter-century. (nature.com)
Dr. Müller-Oehring's research interests aim to advance our understanding on 'how the human brain works' by studying the relationship between brain structure and function using multimodal imaging approaches in healthy and degraded brain systems. (stanford.edu)

Data management and sharing in Large Research Infrastructures: how synchrotrons handle the big data challenge. (upf.edu)

Abstract: Each day, a huge amount of data is generated. (usc.edu)
Abstract: Recent converging advances in sensing and computing allow the ambulatory long-term tracking of individuals yielding a rich set of real-life multimodal bio-behavioral signals, such as speech, physiology, and facial expressions. (usc.edu)

Our research is likely to enable new applications and methods in several related fields which are not traditionally studied jointly, such as digital and public health, computer vision, computing and communication architectures. (oulu.fi)

Parametric atlas methods typically combine these training images into a single atlas image, while nonparametric atlas methods typically use all of the training images separately. (wikipedia.org)
One of these new methods is real-time functional MRI, where people can learn to regulate their own brain states while they are inside the MRI scanner. (interdisciplinary-college.org)
When acquiring new data, these methods are focused on the structural dissimilarity compared to existing data. (nature.com)
However, these methods usually require training of a separate Gaussian process model and rely on the structural representation in latent space. (nature.com)
The theories and different methods of the project will be discussed that include the different sensors used to capture, record, and analyse students physical interactions and how we used this data to create models to understand aspects of collaboration. (upf.edu)
Imaging methods that result in sharp images of objects located on a chosen plane and blurred images located above or below the plane. (lookformedical.com)
METHODS: High-resolution 3D-T1 data were collected from 68 PD-MCI, 211 PD-NC, and 100 matched healthy controls (HC). (bvsalud.org)

The University of Trento ranks among top Italian Universities ( https://www.unitn.it/en/ateneo/1636/rankings ). (python.org)
https://www.discovertrento.it/en A few papers related to the project are below: Bertò G,et al. (python.org)

With the development of artificial intelligence technologies, it is possible to use computer to read digital medical images. (hindawi.com)

This paper proposes a method for the integration of natural language understanding in image classification to improve classification accuracy by making use of associated metadata. (bepress.com)

Besides his dissertation research, Ramyad has contributed to research on processing-in-memory, GPU systems, and hardware accelerators for sparse problems, believing a balance between depth and breadth leads to genuine research problems. (usc.edu)

Beyond viewing and manipulating the EEG data, EPViz allows researchers to load a PyTorch deep learning model, apply it to the EEG, and overlay the output channel-wise or subject-level temporal predictions on top of the original time series. (jhu.edu)
The essence of this update lies in multimodality, where an AI model, in this case, GPT-4, possesses the remarkable capability to process and generate content not only from text inputs but also from images. (typethepipe.com)
From the acquired 2-D projection images, a dynamic 3-D surface model of the LV is generated, which is then used to detect ventricular dyssynchrony. (uni-erlangen.de)
No matter how sophisticated the ML model architecture, however, the quality and diversity of the training data remain crucial for ultimate model accuracy. (nature.com)
Typically, there are five to ten NNs in an ensemble, and these share the same architecture and hyperparameters but, crucially, use a different initial randomization of the model parameters prior to training, as well as different splits of the training/validation data. (nature.com)
An accurate deep learning model for wheezing in children using real world data. (cdc.gov)

At the Big Data Institute , I have established an independent research group that focuses on medical imaging and machine learning. (ox.ac.uk)
My recent research focuses on industrial applications where data are often collected with small sample size. (nottingham.ac.uk)
My research focuses on the amygdala and emotion processing in the human brain. (interdisciplinary-college.org)

Transfusion: Understanding transfer learning for medical imaging. (springer.com)
Computer Vision and Image Understanding , 89(2-3):272-298, Feb.-march 2003. (inria.fr)
We use multiple functional brain mapping techniques (fMRI, diffusion tensor imaging, and intracranial EEG) and structural and molecular imaging techniques to better define individual functional anatomy in patients with neurosurgical diseases, cross-validate information acquired through different techniques, and gain a better understanding of the relationship between these different brain signals. (dana-farber.org)
Our initial findings indicates that this is the case, based on three different perspectives - work practice changes, understanding data quality when sharing information and introducing new services in work practice. (spie.org)
The novelty of this approach was to learn from additional external features associated with the images using natural language understanding with transfer learning. (bepress.com)

The initial 3-D images are all based on retrospective electrocardiogram (ECG)-gated data. (uni-erlangen.de)

The data driven strategy will take advantage of a unique dataset of intra-operative points of directed electrical stimulation and the related functional responses. (python.org)
His major is electrical engineering with technical focus on image processing and remote sensing. (usc.edu)

The acquisition of data for cardiac imaging using a C-arm CT system requires several seconds and multiple heartbeats. (uni-erlangen.de)

Anatomy14

Diseases8

Chemicals and Drugs26

Analytical, Diagnostic and Therapeutic Techniques and Equipment105

Psychiatry and Psychology27

Phenomena and Processes23

Disciplines and Occupations19

Anthropology, Education, Sociology and Social Phenomena3

Technology, Industry, Agriculture17

Humanities3

Information Science60

Health Care35

Geographicals1

Visualization4

Learning algorithms2

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Diagnosis10

Computed tomography6

20215

Detection8

Modalities2

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Segmentation14

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Analysis33

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Medical imaging inf2

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Multidisciplinary1

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20202

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