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Front Matter: Volume 11313

Bennett A. Landman, Ivana Išgum
2020 Medical Imaging 2020: Image Processing  
The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee.  ...  using a Base 36 numbering system employing both numerals and letters.  ...  deep-learning-based image segmentation [11313-38] 11313 14 Weakly supervised pancreas segmentation based on class activation maps [11313-39] 11313 15 Detection of frame informativeness in endoscopic  ... 
doi:10.1117/12.2570657 fatcat:be32besqknaybh6wibz7unuboa

Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear [chapter]

Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant
2018 Lecture Notes in Computer Science  
Infarct on DWI using Weakly Supervised Machine Learning Stefano Pedemonte*; Bernardo Bizzo; Stuart Pomerantz; Neil Tenenholtz; Bradley Wright; Mark Walters; Sean Doyle; Adam McCarthy; Renata Rocha De  ...  -79 A Bayes Hilbert Space for Compartment Model Computing in Diffusion MRI Aymeric Stamm*; Olivier Commowick; Alessandra Menafoglio; Simon Warfield T-80 Detection and Delineation of Acute Cerebral  ...  T-129 Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training Wen  ... 
doi:10.1007/978-3-030-00928-1_1 fatcat:ypoj3zplm5awljf6u5c2spgiea

Asia Pacific Stroke Conference 2016. Abstracts of the Annual Conference of theAsia Pacific Stroke Organization (APSO) Combined with Stroke Society of Australasia, Brisbane, Qld., Australia, July 14-17, 2016: Abstracts

2016 Cerebrovascular Diseases  
In this study we investigate the Gastrodia on the recovery of neurological function in rats of acute middle cerebral artery occlusion.  ...  Conclusion: Gastrodia can promote the recovery of neurological function, and has an effect on the proliferation of brain cells after acute infarction in MCAO rats.  ...  The impact of PP on stroke outcome remains to be delineated.  ... 
doi:10.1159/000447732 pmid:27409083 fatcat:npg6lu4kgzherj7oa5aaory3lq

Deep Learning in Cardiology

Paschalis Bizopoulos, Dimitrios Koutsouris
2019 IEEE Reviews in Biomedical Engineering  
The medical field is creating large amount of data that physicians are unable to decipher and use efficiently.  ...  We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.  ...  Deep learning, and its application on neural networks Deep Neural Networks (DNNs), is a set of machine learning methods that consist of multiple stacked layers and use data to capture hierarchical levels  ... 
doi:10.1109/rbme.2018.2885714 fatcat:pa47trmskvflvig5cotth265q4

Machine Learning in Medical Image Analysis

Liang Chen, Daniel Rueckert, Paul Bentley, National Institute For Health Research
2019
Many algorithms based on machine learning have been applied in medical imaging to solve classification, detection, and segmentation problems.  ...  The second one is learning with weak supervision in the context of medical imaging. The first main contribution of the thesis is a series of novel approaches for image segmentation.  ...  In weakly supervised learning, the aim is to make use of data without labels to improve the performance of supervised learning tasks.  ... 
doi:10.25560/68178 fatcat:3zyz5lihqjb27hpejtaixdizgm

39th Meeting of the Canadian Congress of Neurological Sciences

2004 Canadian Journal of Neurological Sciences  
individuals, safe use of this and all other medications may require supervision.  ...  Acute perfusion (PI) MRI in acute stroke setting might shows regions of hypoperfused cortex associated with lexical deficits or hemispatial neglect, even when diffusion MRI (DWI) shows no infarct or only  ...  Therefore, the maximum daily dose should not exceed 12.5 mg over a 24-hour period, and a starting dose of 6.25 mg should be used (see ACTION AND CLINICAL PHARMACOLOGY, Special Populations and PRECAUTIONS  ... 
doi:10.1017/s0317167100002961 fatcat:3umzmupwt5b6vc2ec5akfgbms4

Posters

2011 Pediatric Radiology  
Agreement within groups, split by grade and specialty, was analysed using free-marginal multirater Kappa, assuming no prior expectation of the proportion of radiographs with each feature.  ...  PCH-2 Illustrative and educational perspective of cystic lung lesion in children  ...  MRI has the highest sensitivity and specificity in the detection of marrow and extraosseous changes in both infarction and infection.  ... 
doi:10.1007/s00247-011-2025-3 fatcat:4kgxnlpo2bes5phwdwsxu74j2y

Posters

2013 Clinical and translational imaging  
cerebral metabolic uptake. 18 F-DOPA/ 18 F-choline PET/CT (FDOPA/FCH) seem to be useful diagnostic tools.  ...  Aim The aim of our study was to establish the prevalence, clinical significance and pathological nature of focal thyroid incidentalomas detected on 18 F-FDG-PET/CT in patients studied for oncological purposes  ...  the site of the primary acute event (acute myocardial infarction, AMI) and the extent of denervation.  ... 
doi:10.1007/s40336-013-0002-6 fatcat:jfrptu6wuvhjvfss4iobwqfk2q

CARS 2016—Computer Assisted Radiology and Surgery Proceedings of the 30th International Congress and Exhibition Heidelberg, Germany, June 21–25, 2016

2016 International Journal of Computer Assisted Radiology and Surgery  
We would like to thank the Spanish company BQ for the donation of the 3D printing hardware for clinical use.  ...  Acknowledgments The authors wish to thank Fundación CEIBA and Alcaldía Mayor de Bogotá, for the financial support of Ricardo Mendoza's PhD studies through the scholarship program ''Becas Rodolfo Llinás  ...  Methods We have chosen supervised learning [2] for training the models that are used for the detection of the actual progress of a surgical intervention.  ... 
doi:10.1007/s11548-016-1412-5 pmid:27206418 fatcat:uk5r46n2xvhedkfjzmeiweyneq

A combined local and global motion estimation and compensation method for cardiac CT

Qiulin Tang, Beshan Chiang, Akinola Akinyemi, Alexander Zamyatin, Bibo Shi, Satoru Nakanishi, Bruce R. Whiting, Christoph Hoeschen
2014 Medical Imaging 2014: Physics of Medical Imaging  
(United States) The evaluation and treatment of acute cerebral ischemia requires a technique that can determine the total area of tissue at risk for infarction using diagnostic magnetic resonance imaging  ...  machine learning methods applied to multiparametric MRI in cerebral ischemia: preliminary results Vishwa S.  ...  This is a manual process and can be time consuming in cases where several sections using different stains are required.  ... 
doi:10.1117/12.2043492 fatcat:fyzpc5m6jbh7fjohqpdmtzkhte

ACTRIMS-ECTRIMS MSBoston 2014: Poster Sessions 2

2014 Multiple Sclerosis  
Conclusions: This newly developed method detects change in UCCA over the relatively short period of one year in both SP and PPMS patients.  ...  An alternative approach to support a diagnosis of MS using MRI may be to detect a histological feature characteristic of MS in vivo.  ...  We used the machine learning technique, support vector machines (SVM), to build prediction models classifying MS patients into those with or without progression during the follow-up period.  ... 
doi:10.1177/1352458514547846 pmid:25205049 pmcid:PMC4244175 fatcat:gj757y4jlvejpnrfw22guoit7m

49th Annual Meeting of the Association for European Paediatric and Congenital Cardiology, AEPC with joint sessions with the Japanese Society of Pediatric Cardiology and Cardiac Surgery, Asia-Pacific Pediatric Cardiology Society, European Association for Cardio-Thoracic Surgery and Canadian Pediatric Cardiology Association, Prague, Czech Republic, 20–23 May 2015

2015 Cardiology in the Young  
Three of them were prescribed aspirin, but one of them and the other patient were lost to follow-up of our university hospital.  ...  One is acceleration of coagulation, and the other is increased heparin clearance. Those problems are more serious for KD patients with CAL.  ...  None of the patients had diffusion restricted cerebral lesions resembling micro embolic infarctions.  ... 
doi:10.1017/s1047951115000529 fatcat:5lmcvpcmcjenbbw372jaocciue

Multimodal and disentangled representation learning for medical image analysis [article]

Agisilaos Chartsias, University Of Edinburgh, Sotirios Tsaftaris, Javier Escudero Rodriguez
2021
In this thesis, we argue that their success and generalisation relies on learning good latent representations.  ...  In order to evaluate the benefit of multimodal learning, we initially consider a synthesis task on spatially aligned multimodal brain MR images.  ...  Furthermore, super-resolution has been coupled with cross-modal synthesis using dictionary learning: with the addition of unpaired data in the learning process, a weakly supervised learning approach has  ... 
doi:10.7488/era/767 fatcat:25dlmeyl2rfdnaugsk3hdox7gy

Methods for functional connectivity and morphometry in neonatal neuroimaging to study neurodevelopment

Serafeim Loukas
2021
We also thank Division of ENT, the Plateforme de Recherche de Pédiatrie and the Centre for Biomedical Imaging (CIBM) of the University Hospital of Geneva for their support.  ...  We also thank Division of ENT, the Plateforme de Recherche de Pédiatrie, and the Centre for Biomedical Imaging (CIBM) of the University Hospital of Geneva for their support.  ...  An SVM model is a supervised machine learning model that finds the best separating hyperplane between two classes (Hearst et al., 1998) .  ... 
doi:10.5075/epfl-thesis-8854 fatcat:tkk4kcdvhfcsnnsbizakqgcwky

Bayesian modeling of brain function and behavior

Maya Anna Jastrzebowska
2020
Acknowledgments, Funding and Financial Disclosure Statement We would like to thank all those who participated in the study, Dr Deepa Pothalil and Dr Elisabeth Roggenhofer for clinical evaluations.  ...  Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning; 2016. p. 1050-1059.  ...  Our work was inspired by the growing emphasis on parameter uncertainty in the machine learning community; however, it is important to highlight that function learning and extrapolation have been studied  ... 
doi:10.5075/epfl-thesis-7388 fatcat:ypuovx46svhttmwjvup57msbla