• EPViz also provides valuable tools for clinician-scientists, including spectrum visualization, computation of basic statistics, data anonymization, and annotation editing. (jhu.edu)
  • General topics of the conference include medical image computing, computer-assisted intervention, guidance systems and robotics, visualization and virtual reality, computer-aided diagnosis, bioscience and biology applications, specific imaging systems, and new imaging applications. (wikipedia.org)
  • 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)
  • The Betti plots are the basic data visualization technique in persistent homology but statistical inference procedure has been lacking in the field [1,2,3]. (wisc.edu)
  • Efforts are underway to demonstrate advanced combustion visualization capabilities using combinations of these diagnostics and publish novel, fundamental, useful combustion data. (sysplus.com)
  • National awards include National Academy of Sciences Jefferson Science Fellowship and the NASA Public Service Medal for big data visualization. (researchgate.net)
  • At NASA GSFC he co-founded the Visualization Analysis Lab and co-developed the Interactive Image SpreadSheet for very large imagery. (researchgate.net)
  • In our project NARVIS we integrate an HMD-based (head mounted display) AR system into the operation room for 3D in situ visualization of computed tomography (CT) images. (tum.de)
  • Furthermore, image analysis provides a simple and noninvasive visualization of the retinal blood vessels in those high risk ophthalmologic medical conditions [ 1 - 3 ]. (hindawi.com)
  • IEEE International Symposium on Biomedical Imaging (ISBI). (childrenshospital.org)
  • MOAB: Multi-modal Outer Arithmetic Block for Fusion of Histopathological Images and Genetic Data for Brain Tumor Grading , Omnia Alwazzan, Abbas Khan, Yiannis Patras, Gregory Slabaugh, International Symposium on Biomedical Imaging (ISBI) , 2023. (qmul.ac.uk)
  • 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)
  • Remote sensing image intelligent interpretation: from supervised learning to self-supervised learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8): 1122-1134. (chinasmp.com)
  • CURL: Neural Curve Layers for Global Image Enhancement , Sean Moran, Steven McDonagh, Gregory Slabaugh, International Conference on Pattern Recognition (ICPR) , 2021. (qmul.ac.uk)
  • 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)
  • Technologies that increase the speed and accuracy of stroke diagnosis or assist in post-stroke rehabilitation can improve patient outcomes. (mdpi.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)
  • The chair aims at providing creative physicians with the technology and partnership, which allow them to introduce new diagnosis, therapy and surgical techniques taking full advantage of advanced computer technology. (tum.de)
  • Virtual patients are able to represent patients in realistic clinical scenarios and engage learners in doctor-patient conversations about the patient's health, interpret laboratory results and medical images, and form a diagnosis. (pub.ro)
  • Artificial Intelligence Algorithm-Based Analysis of Ultrasonic Imaging Features for Diagnosis of Pregnancy Complicated with Brain Tumor. (cdc.gov)
  • Deep Learning Assisted Diagnosis of Musculoskeletal Tumors Based on Contrast-Enhanced Magnetic Resonance Imaging. (cdc.gov)
  • End-to-End Neural Network for Feature Extraction and Cancer Diagnosis of In Vivo Fluorescence Lifetime Images of Oral Lesions. (cdc.gov)
  • 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)
  • ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. (uzh.ch)
  • Deep neural networks are commonly used for medical purposes such as image generation, segmentation, or classification. (catalyzex.com)
  • Joint Dense-Point Representation for Contour-Aware Graph Segmentation , Kit Bransby, Qianni Zhang, Gregory Slabaugh, Christos Bourantas, Medical Image Analysis and Computer-Aided Interventions (MICCAI) , 2023. (qmul.ac.uk)
  • Sequential Segmentation of the Left Atrium and Atrial Scars Using a Multi-scale Weight Sharing Network and Boundary-based Processing , Abbas Khan, Omnia Alwazzan, Martin Benning, Greg Slabaugh, Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop, Left Atrial and Scar Quantification & Segmentation Challenge (LAScarQS) Challenge, Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022. (qmul.ac.uk)
  • Accurate Automatic Glioma Segmentation in Brain MRI images Based on CapsNet. (cdc.gov)
  • 2022). Physiological effects of human body imaging with 300 mT/m gradients. (edu.au)
  • FlexHDR: Modelling Alignment and Exposure Uncertainties for Flexible HDR Imaging , Sibi Catley-Chandar, Thomas Tanay, Lucas Vandroux, Aleš Leonardis, Gregory Slabaugh, Eduardo Pérez-Pellitero, IEEE Transactions on Image Processing (T-IP) , 2022. (qmul.ac.uk)
  • In ACM Transactions on Computer-Human Interaction , pages 36, number 1, March 2023. (rwth-aachen.de)
  • MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 12267:437-447, 2020. (jhu.edu)
  • 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)
  • 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)
  • Executive summary of the report on earth observation data resources of China (2019)[R]. Beijing:Aerospace Information Research Institute, Chinese Academy of Sciences, 2020:1-34. (chinasmp.com)
  • The Computer Assisted Radiology and Surgery (CARS) congress is the CARS annual conference. (wikipedia.org)
  • Information literacy International Society for Computer Assisted Surgery (ISCAS) International Conference on Information Processing in Computer-Assisted Interventions (IPCAI) The Computer Assisted Radiology and Surgery (CARS) congress Linte, Cristian A. (wikipedia.org)
  • Dr. Rusu is an Assistant Professor, in the Department of Radiology, and, by courtesy, Department of Urology and Biomedical Data Science, at Stanford University, where she leads the Personalized Integrative Medicine Laboratory (PIMed). (stanford.edu)
  • The PIMed Laboratory has a multi-disciplinary direction and focuses on developing analytic methods for biomedical data integration, with a particular interest in radiology-pathology fusion to facilitate radiology image labeling. (stanford.edu)
  • Dr. Mirabela Rusu focuses on developing analytic methods for biomedical data integration, with a particular interest in radiology-pathology fusion. (stanford.edu)
  • Sudhin Shah, Ph.D., is an assistant professor of neuroscience in the department of radiology within the Brain Health and Imaging Institute at Weill Cornell Medicine (WCM). (cornell.edu)
  • 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)
  • 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)
  • 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)
  • Different quantitative features to evaluate LV dynamics known from other modalities (ultrasound, MR) are transferred to the C-arm CT data. (uni-erlangen.de)
  • Understanding the data generation process could help to create artificial medical data sets without violating patient privacy, synthesizing different data modalities, or discovering data generating characteristics. (catalyzex.com)
  • Augmented reality techniques can be applied to superimpose pre-operative imaging data from modalities such as CT, MRI, or US onto the endoscopic video images to compensate for these limitations. (tum.de)
  • Proceedings for this conference are published by Springer in the Lecture Notes in Computer Science series. (wikipedia.org)
  • 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)
  • Lecture Notes in Computer Science, vol 11824. (catalyzex.com)
  • 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)
  • Structural magnetic resonance imaging scans were acquired in 37 patients (mean age 42 years) and 37 age, gender and IQ-matched healthy individuals. (uel.ac.uk)
  • This work introduces a novel framework, referred to as microscopic susceptibility anisotropy imaging, that disentangles the 2 principal effects conflated in gradient‐echo measurements, (a) the susceptibility properties of tissue microenvironments, especially the myelin microstructure, and (b) the axon orientation distribution relative to the magnetic field. (edu.au)
  • The lab conducts clinical translational studies in both adult and pediatric brain injury, employing clinically feasible neurophysiological tools- the electroencephalogram (EEG) and transcranial direct current stimulation (tDCS)-alongside state-of-the-art neuroimaging tools (magnetic resonance imaging (MRI) and positron emission tomography (PET) ligand studies. (cornell.edu)
  • 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)
  • Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. (springer.com)
  • Additionally, we contribute the first 'multimodal light field depth dataset' that contains the depths of all objects which contribute to the color of a pixel. (catalyzex.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)
  • 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)
  • IEEE Transactions on Medical Imaging , 22(10):1185-1201, October 2003. (inria.fr)
  • IEEE Transactions on Medical Imaging , 22(9):1120-30, September 2003. (inria.fr)
  • IEEE Transactions on Image Processing , 32 , 4800 -4811. (swansea.ac.uk)
  • Automated detection of COVID-19 through convolutional neural network using chest X-ray images. (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)
  • Federated learning enables big data for rare cancer boundary detection. (uzh.ch)
  • 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)
  • As a methodological novelty, it adopts key principles from the field of object detection validation, which has a long history of addressing the question of how to locate and match multiple object instances in an image. (catalyzex.com)
  • Early Detection and Intervention for Cerebral Palsy (EDI4CP). (hrb.ie)
  • This allows us to supervise the multimodal depth prediction and also validate all methods by measuring the KL divergence of the predicted posteriors. (catalyzex.com)
  • The goal is to demonstrate AI-enabled prediction, prevention and intervention, making the treatment of disorders such as Parkinson's, multiple sclerosis and strokes more affordable and easing the burden on healthcare systems across Europe. (pub.ro)
  • In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. (crossref.org)
  • a free, non-commercial open science client-server framework for (bio-)medical image analysis. (springer.com)
  • We demonstrate the value of our framework through instantiations for a synthetic toy example and two medical vision use cases: pose estimation in surgery and imaging-based quantification of functional tissue parameters for diagnostics. (catalyzex.com)
  • The goal is to pursue an integrated approach to pre-operative neurosurgical planning, combining structural and functional characterization of brain connectivity. (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)
  • The average functional network pattern is stored in networkfMRI.mat , which stores the strength of connectivity between 116 regions using Pearson correlations. (wisc.edu)
  • The position is part of the "Neusurplan" project, an integrated approach to neurosurgery planning based on multimodal and longitudinal data. (python.org)
  • Therefore, myocardial wall analysis is completely based on the 2-D angiographic images or pre-interventional 3-D/4-D imaging. (uni-erlangen.de)
  • Imaging data suggests that dialysis increases myocardial blood flow (BF) heterogeneity, but its causes remain poorly understood. (frontiersin.org)
  • Although non-invasive interventions may not affect existing large vessel structural defects such as stenosis, it is thought that adenosine stress and dialysate cooling therapy may improve myocardial BF by vasodilation of the smaller blood vessels. (frontiersin.org)
  • Despite the tremendous acute and chronic impact of cognitive impairments post injury, efforts to accurately diagnose, sensitively prognosticate and track therapeutic interventions remain limited. (cornell.edu)
  • Various diagnostic techniques are used to analyze retinal microvasculature image to enable geometric features measurements such as vessel tortuosity, branching angles, branching coefficient, vessel diameter, and fractal dimension. (hindawi.com)
  • These extracted markers or characterized fundus digital image features provide insights and relates quantitative retinal vascular topography abnormalities to various pathologies such as diabetic retinopathy, macular degeneration, hypertensive retinopathy, transient ischemic attack, neovascular glaucoma, and cardiovascular diseases. (hindawi.com)
  • This paper will also review recent research on the image processing-based extraction techniques of the quantitative retinal microvascular feature. (hindawi.com)
  • One of the most important subfields of biomedical engineering is the analysis of fundus retinal images. (hindawi.com)
  • Analysis of the human fundus eye images has become the key point for diagnosing the various pathologies of retinal vasculature. (hindawi.com)
  • The fundus retinal images are directly captured from human eye that includes some other landmarks like microcirculation system of the retina, macula, optic disc, fovea, microaneurysm, and exudates [ 4 ]. (hindawi.com)
  • This cost-effective, simple image acquisition system can be used in the large-scale screening programs and retinal image analysis developing mathematical and computational techniques. (hindawi.com)
  • At the University of Missouri he established the Computational Imaging and Vis-Analysis Laboratory. (researchgate.net)
  • 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)
  • We present a pipeline for predicting mechanical properties of vertically-oriented carbon nanotube (CNT) forest images using a deep learning model for artificial intelligence (AI)-based materials discovery. (researchgate.net)
  • The ReIMAGINE Multimodal Warehouse: Using Artificial Intelligence for Accurate Risk Stratification of Prostate Cancer. (cdc.gov)
  • Randomized trial of two artificial intelligence coaching interventions to increase physical activity in cancer survivors. (cdc.gov)
  • She continued her training at the University of Texas Health Science Center in Houston, where she received a Master of Science and PhD degree in Health Informatics for her work in biomolecular structural data integration of cryo-electron micrographs and X-ray crystallography models. (stanford.edu)
  • scQUEST: Quantifying tumor ecosystem heterogeneity from mass or flow cytometry data. (uzh.ch)
  • A biophysical model of human coronary vasculature was used to explain the imaging observations, and highlight causes of coronary BF heterogeneity. (frontiersin.org)
  • Imaging showed that stress and therapy potentially increased mean and total BF, while reducing heterogeneity. (frontiersin.org)
  • However, this method heavily relies on large-scale and high-quality labeled data, while building a big remote sensing data set is extremely expensive because of the unique spatial-temporal heterogeneity of remote sensing data. (chinasmp.com)
  • 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)
  • 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)
  • 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)
  • 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 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)
  • Note: Special Issue on Ultrasonic Image Processing and Analysis. (inria.fr)
  • Medical Image Analysis , 7(4):475-488, December 2003. (inria.fr)
  • The proposed analysis tool was evaluated on simulated phantom LV data with and without pathological wall dysfunctions. (uni-erlangen.de)
  • Medical Image Analysis, 84:102680. (uzh.ch)
  • The recent focus of the lab has been on applying deep learning methods to detect and differentiate aggressive from indolent prostate cancers on MRI using the pathology information (both labels and the image content), work that was recently published in Medical Physics and Medical Image Analysis Journals. (stanford.edu)
  • Prior to joining Stanford, Dr. Rusu was a Lead Engineer and Medical Image Analysis Scientist at GE Global Research Niskayuna NY where she was involved in the development of analytic methods to characterize biological samples in microscopy images and pathologic conditions in MRI or CT. (stanford.edu)
  • Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist. (catalyzex.com)
  • Recent advances in computer vision and machine learning technology have enabled the automated analysis of coronary angiography. (jmir.org)
  • In the present modern ophthalmological practice, biomarkers analysis through digital fundus image processing analysis greatly contributes to vision science. (hindawi.com)
  • Computer-assisted interventions (CAI) is a field of research and practice, where medical interventions are supported by computer-based tools and methodologies. (wikipedia.org)
  • This paper aims to identify whether health care staff perceive a 12-week online facilitated, multimodal, person-centred care, dementia education program influences their knowledge, skills, behaviour and practice improvement activities in dementia care. (biomedcentral.com)
  • 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)
  • 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)
  • Transfusion: Understanding transfer learning for medical imaging. (springer.com)
  • Computer Vision and Image Understanding , 89(2-3):272-298, Feb.-march 2003. (inria.fr)
  • Such integrative methods may be applied to create comprehensive multi-scale representations of biomedical processes and pathological conditions, thus enabling their in-depth characterization. (stanford.edu)
  • METHODS: A MFR model combining macroscopic MRI and microscopic pathological images was proposed. (bvsalud.org)
  • Using the data, we will show how to construct Betti plots from weighted brain graphs, where the weights are Pearson correlations. (wisc.edu)
  • It is the average functional network of 416 normal subjects and considered as the representative functional brain networks at rest when people are not doing any task. (wisc.edu)
  • Dr. Palaniappan is a professor of Electrical Engineering and Computer Science at the University of Missouri. (researchgate.net)
  • International Conference on Information Processing in Computer-Assisted Interventions (IPCAI) is a premiere international forum for technical innovations, system development and clinical studies in computer-assisted interventions. (wikipedia.org)
  • The deformations may reflect structural correlates underlying functional memory impairments and distinguish depression from other psychiatric disorders. (uel.ac.uk)
  • Breast cancer classification of image using convolutional neural network. (springer.com)
  • Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging. (cdc.gov)
  • Autoencoders are able to learn useful data representations in an unsupervised matter and have been widely used in various machine learning and computer vision tasks. (catalyzex.com)
  • Digital image processing is one of the most widely used computer vision technologies in biomedical engineering. (hindawi.com)
  • Moreover, our project are interested in further develop these approaches for ultrasound images. (stanford.edu)
  • During her postdoctoral training at Rutgers and Case Western Reserve University, Dr. Rusu has developed computational tools for the integration and interpretation of multi-modal medical imaging data and focused on studying prostate and lung cancers. (stanford.edu)
  • Specifically, we utilize information about the orientational tissue structure inferred from diffusion MRI data to factor out the urn:x-wiley:07403194:media:mrm28303:mrm28303-math-0008‐direction dependence of the frequency difference signal. (edu.au)
  • The ideal candidate should have a mixed background in neuroimaging techniques and numerate disciplines, like computer science, engineering, physics, or mathematics. (python.org)
  • Unpaired image-to-image translation using cycle-consistent adversarial networks. (crossref.org)