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Front Matter: Volume 10574

Proceedings of SPIE, Elsa D. Angelini, Bennett A. Landman
2018 Medical Imaging 2018: Image Processing  
using a Base 36 numbering system employing both numerals and letters.  ...  Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  the teacher: deep neural networks for lateral ventricles segmentation in brain MR [10574-99] 10574 2V Fully convolutional neural networks improve abdominal organ segmentation (Cum Laude Poster Award)  ... 
doi:10.1117/12.2315755 fatcat:jdfbaent6vhu5dwlrqrqt66vce

Quantification of Cognitive Function in Alzheimer's Disease Based on Deep Learning

Yanxian He, Jun Wu, Li Zhou, Yi Chen, Fang Li, Hongjin Qian
2021 Frontiers in Neuroscience  
This paper proposes a cascaded three-dimensional neural network framework based on single-modal and multi-modal images, using MRI and PET images to distinguish AD and MCI from normal samples.  ...  This paper uses graph theory to analyze the constructed brain network, and extracts the node degree, node efficiency, and node betweenness centrality parameters of the two modal brain networks.  ...  Relevant scholars use principal component analysis to reduce the dimensionality of MRI images, and then use linear regression models to predict the development trend of the MMSE scale for patients with  ... 
doi:10.3389/fnins.2021.651920 pmid:33815051 pmcid:PMC8010261 fatcat:hno2ujvmf5dzpdafiwelbjstbe

3D Regression Neural Network for the Quantification of Enlarged Perivascular Spaces in Brain MRI [article]

Florian Dubost, Hieab Adams, Gerda Bortsova, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
2018 arXiv   pre-print
We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI.  ...  We validated our approach using a dataset of 2000 brain MRI scans scored visually.  ...  This research was funded by The Netherlands Organisation for Health Research and Development (ZonMw) Project 104003005.  ... 
arXiv:1802.05914v2 fatcat:pysgwj6tframzhsfmk5oasrsxy

Enlarged perivascular spaces in brain MRI: Automated quantification in four regions

Florian Dubost, Pinar Yilmaz, Hieab Adams, Gerda Bortsova, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
2019 NeuroImage  
Our approach is a deep learning algorithm based on convolutional neural network regression, and is contingent on successful brain structure segmentation. In our work we used FreeSurfer segmentations.  ...  A B S T R A C T Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, are common in aging, and are considered a reflection of cerebral small vessel disease.  ...  This research was funded by The Netherlands Organisation for Health Research and Development (ZonMw) Project 104003005, with additional support of Netherlands Organisation for Scientific Research, project  ... 
doi:10.1016/j.neuroimage.2018.10.026 pmid:30326293 fatcat:yvlkoeeyw5cvlg63sxq6opltxy

FDCNet: Presentation of the Fuzzy CNN and Fractal Feature Extraction for Detection and Classification of Tumors

Sepideh Molaei, Niloofar Ghorbani, Fatemeh Dashtiahangar, Mohammad Peivandi, Yaghoub Pourasad, Mona Esmaeili, Mohamed Abdelaziz
2022 Computational Intelligence and Neuroscience  
neural network.  ...  Creating an intelligent medical diagnosis system for the diagnosis of brain tumors from MRI imaging is an integral part of medical engineering as it helps doctors detect brain tumors early and oversee  ...  Also, deep neural network convolution is used by amplifying extensive data in the diagnosis of brain tumors from MRI images and BraTS data sets in [9] .  ... 
doi:10.1155/2022/7543429 pmid:35571692 pmcid:PMC9106477 fatcat:j6bxfwyifrb2fhxv2oxxircydq

Natural Language Processing for Automated Quantification of Brain Metastases Reported in Free-Text Radiology Reports

Joeky T. Senders, Aditya V. Karhade, David J. Cote, Alireza Mehrtash, Nayan Lamba, Aislyn DiRisio, Ivo S. Muskens, William B. Gormley, Timothy R. Smith, Marike L.D. Broekman, Omar Arnaout
2019 JCO Clinical Cancer Informatics  
A variety of natural language processing (NLP) methods have emerged to automate the processing of free text ranging from statistical to deep learning-based models; however, the optimal approach for medical  ...  The aim of this study was to provide a head-to-head comparison of novel NLP techniques and inform future studies about their utility for automated medical text analysis.  ...  among the bag-of-words models and 1D-convolutional neural networks demonstrated superior overall performance among the sequence-based models.  ... 
doi:10.1200/cci.18.00138 pmid:31002562 pmcid:PMC6873936 fatcat:4tqds46nj5cyhbekyfu7bxzhzm

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be  ...  We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  TGCN model: Traditional temporal convolutional neural networks (TCNN) show that variations of convolutional neural networks can achieve impressive results for sequential data [65] .  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling [article]

Tim Hahn, Jan Ernsting, Nils R. Winter, Vincent Holstein, Ramona Leenings, Marie Beisemann, Lukas Fisch, Kelvin Sarink, Daniel Emden, Nils Opel, Ronny Redlich, Jonathan Repple (+22 others)
2021 arXiv   pre-print
Here, we introduce an uncertainty-aware, shareable, and transparent Monte-Carlo Dropout Composite-Quantile-Regression (MCCQR) Neural Network trained on N=10,691 datasets from the German National Cohort  ...  The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a cornerstone of biological  ...  The MACS dataset used in this work is part of the German multicenter consortium "Neurobiology of Affective Disorders.  ... 
arXiv:2107.07977v1 fatcat:fdx6dgeahba2xmc3ik7hvzemou

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be  ...  We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

Review on Early Detection of Alzheimer's Disease using Neuroimaging Techniques

Vishnu N, R Vaidya, Chaitra N, Srinidhi S P, Shreyas B
2021 Zenodo  
Machine learning, neuroimaging, and deep learning neural networks are few of the techniques which are compared and analysed based on their performance and accuracy.  ...  Alzheimer's disease (AD) is the most common form of dementia. AD begins slowly, where it first involves a part of the brain that controls thought, memory, and language.  ...  in association with 3D convolutional neural networks.  ... 
doi:10.5281/zenodo.4420080 fatcat:56eec7xvc5hxbgihmqmvz7gsxu

Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI across Sites [article]

Florian Dubost, Max Duennwald, Denver Huff, Vincent Scheumann, Frank Schreiber, Meike Vernooij, Wiro Niessen, Martin Skalej, Stefanie Schreiber, Steffen Oeltze-Jafra, Marleen de Bruijne
2019 bioRxiv   pre-print
Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, and are a marker of cerebral small vessel disease.  ...  We evaluate and compare two recently published automated methods for the quantification of enlarged perivascular spaces in 76 clinical scans acquired from 9 different scanners.  ...  Acknowledgements This work received funding from the Netherlands Organisation for Health Research and Development (ZonMw -Project 104003005) and the federal state of Saxony-Anhalt, Germany (Project I 88  ... 
doi:10.1101/738955 fatcat:f53n7ljbnrghfjuw4r4yiyei7i

An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling

Tim Hahn, Jan Ernsting, Nils R. Winter, Vincent Holstein, Ramona Leenings, Marie Beisemann, Lukas Fisch, Kelvin Sarink, Daniel Emden, Nils Opel, Ronny Redlich, Jonathan Repple (+21 others)
2022 Science Advances  
For comparison, we also evaluated a version of our neural network model without uncertainty quantification but with an otherwise identical network structure and hyperparameters [artificial neural network  ...  A recent study also recognized the issue of uncertainty quantification for brain-age modeling and used quantile regression (QR) to estimate aleatory uncertainty in brain-age prediction (20) .  ...  The MCCQR BrainAge model based on the GNC data as used in this study is available from https://photon-ai.com/model_repo/uncertainty-brain-age.  ... 
doi:10.1126/sciadv.abg9471 pmid:34985964 pmcid:PMC8730629 fatcat:zeupkpspqjahfptcixzbz6rt3a

Deep into the Brain: Artificial Intelligence in Stroke Imaging

Eun-Jae Lee, Yong-Hwan Kim, Namkug Kim, Dong-Wha Kang
2017 Journal of Stroke  
Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial.  ...  In this review, we offer a glimpse at the use of AI in stroke imaging, specifically focusing on its technical principles, clinical application, and future perspectives.  ...  (D) Convolutional neural network. Weighted connections are indicated with the same color in convolutional hidden layers.  ... 
doi:10.5853/jos.2017.02054 pmid:29037014 pmcid:PMC5647643 fatcat:sbvi7boytndjfkk7em57aopqse

An overview of deep learning in medical imaging focusing on MRI

Alexander Selvikvåg Lundervold, Arvid Lundervold
2018 Zeitschrift für Medizinische Physik  
The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks.  ...  Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry.  ...  Acknowledgements We thank Renate Grüner for useful discussions. The anonymous reviewers gave us excellent constructive feedback that led to several improvements throughout the article.  ... 
doi:10.1016/j.zemedi.2018.11.002 fatcat:kkimovnwcrhmth7mg6h6cpomjm

Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review [article]

Jie Yuan, Xuming Ran, Keyin Liu, Chen Yao, Yi Yao, Haiyan Wu, Quanying Liu
2021 arXiv   pre-print
neural networks and autoencoders.  ...  We start with an overview of epilepsy and typical neuroimaging modalities used in epilepsy clinics, MRI, DWI, fMRI, and PET.  ...  For instance, convolutional autoencoders were stacked to form the siamese network for learning the representation of each patch at the same spatial localization in healthy brain MRIs.  ... 
arXiv:2102.03336v3 fatcat:mryusowfbjfjldmx46zcwu6dja
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