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Multi-scale semi-supervised clustering of brain images: deriving disease subtypes
[article]
2021
bioRxiv
pre-print
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Clustering methods have gained popularity in stratifying patients into subpopulations (i.e., subtypes) of brain diseases using imaging data. ...
Herein we propose a novel method, Multi-scAle heteroGeneity analysIs and Clustering (MAGIC), to depict the multi-scale presentation of disease heterogeneity, which builds on a previously proposed semi-supervised ...
First, to address the aforementioned multi-scale limitations, we propose a data-driven and multi-scale semi-supervised method termed MAGIC for "Multi-scAle heteroGeneity analysIs and Clustering". ...
doi:10.1101/2021.04.19.440501
fatcat:q7it24mssfcg7dsyp6kt6wha4a
Subtyping brain diseases from imaging data
[article]
2022
arXiv
pre-print
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The current chapter focuses on ML methods, especially semi-supervised clustering, that seek disease subtypes using imaging data. ...
Work from Alzheimer Disease and its prodromal stages, psychosis, depression, autism, and brain cancer are discussed. ...
Semi-supervised clustering Semi-supervised clustering methods dissect the subtle heterogeneity of interest under the principle of deriving data-driven and neurobiologically plausible subtypes (Figure ...
arXiv:2202.10945v1
fatcat:3sqq4lrilnguzgbm4iwtzm7wc4
Front Matter: Volume 10578
2018
Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
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The diverse sessions included MRI and fMRI, Keynote and Emerging Trends, Neurological Imaging, Cardiovascular Imaging, Novel Imaging Techniques and Applications, Innovations in Image Processing, Optical ...
, Cancer, Imaging Agents, and Bone and Musculoskeletal. ...
material properties on the wall stress distribution of abdominal aortic aneurysms Determining disease evolution driver nodes in dementia networks 10578 2A Semi-supervised sparse representation classifier ...
doi:10.1117/12.2323952
fatcat:om4wezsn3vgr7mebecadzuy5ly
MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases
[article]
2020
arXiv
pre-print
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We first extract multi-scale patterns of structural covariance (PSCs) followed by a semi-supervised clustering with double cyclic block-wise optimization across different scales of PSCs. ...
Recent semi-supervised clustering techniques have provided a data-driven way to understand disease heterogeneity. ...
Alternatively, several recent techniques have been proposed to utilize semi-supervised clustering to distinguish heterogeneous disease effects. ...
arXiv:2007.00812v2
fatcat:suw7txtgw5gvrd2jjuagv4fijm
Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review
[article]
2021
arXiv
pre-print
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Medical imaging is widely used in cancer diagnosis and treatment, and artificial intelligence (AI) has achieved tremendous success in various tasks of medical image analysis. ...
Specifically, we categorize the AI-based methods by training strategy: supervised and unsupervised. ...
CONFLICT OF INTEREST The authors declare no conflicts of interest. ...
arXiv:2103.13588v1
fatcat:3fxgny7u3bcxzcmlkhzz5fdvv4
Radiological images and machine learning: Trends, perspectives, and prospects
2019
Computers in Biology and Medicine
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2019 pre-print arXiv:1903.11726v1 fatcat:bwpwqz3fc5asxp644hhzvslhie
and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems. ...
This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies ...
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ...
doi:10.1016/j.compbiomed.2019.02.017
pmid:31054502
pmcid:PMC6531364
fatcat:tcyorm6g3ff6dg7ty2ubtqorjq
Artificial Intelligence in Glioma Imaging: Challenges and Advances
[article]
2020
arXiv
pre-print
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that can be extracted from brain imaging. ...
Patients who are suspected to have a brain tumor will be assessed with computed tomography (CT) and magnetic resonance imaging (MRI). ...
Acknowledgments Partial funding for this project is provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) and Simon Fraser University Big Data The Next Big Question Fund. ...
arXiv:1911.12886v2
fatcat:ggzcxfzwvzdxxpqof32kh2ut7y
JointMMCC: Joint Maximum-Margin Classification and Clustering of Imaging Data
2012
IEEE Transactions on Medical Imaging
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We describe a Joint Maximum-Margin Classification and Clustering (JointMMCC) approach that jointly detects the pathologic population via semi-supervised classification, as well as disentangles heterogeneity ...
of the pathological cohort by solving a clustering subproblem. ...
Acknowledgments This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging (NIA), and R01-AG14971, N01-AG-3-2124, N01-AG-3-2124. ...
doi:10.1109/tmi.2012.2186977
pmid:22328179
pmcid:PMC3386308
fatcat:nxmow5rjgvbofakpruztb5vy7m
Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications
[article]
2022
arXiv
pre-print
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Magnetic resonance imaging (MRI) provides detailed in-vivo structural information, permitting the quantification and categorization of MS lesions that critically inform disease management. ...
Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the appearance of focal lesions in the white and gray matter that topographically ...
The majority of state-of-theart work in the field is therefore focused on either unsupervised segmentation or semi-supervised fine-tuning with domain adaptation strategies. ...
arXiv:2104.10029v3
fatcat:elds3foafrdc5ireld5wahp5ra
The Stroke Neuro-Imaging Phenotype Repository: An Open Data Science Platform for Stroke Research
2021
Frontiers in Neuroinformatics
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characteristics and disease-specific features from contributed images, (iii) facilitate integration of imaging, genomic, and clinical data to perform large-scale analysis of complications after stroke ...
Millions of stroke patients undergo routine brain imaging each year, capturing a rich set of data on stroke-related injury and outcomes. ...
ACKNOWLEDGMENTS The authors would like to thank Stephen Moore, William Horton, Rick Herrick, and Matt Kelsey, and others on the XNAT development team at the Washington University School of Medicine and ...
doi:10.3389/fninf.2021.597708
pmid:34248529
pmcid:PMC8264586
fatcat:ch3ofmaunvafffxjfgy5epfy4q
Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data
[article]
2019
bioRxiv
pre-print
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Such images represent the cutting edge of digital histology and promise to revolutionize how diseases such as cancer are studied, diagnosed, and treated. ...
Facetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. ...
This work is supported by the Ludwig Center at Harvard Medical School, by NCI Grant U54-CA225088, by King Abdullah University of Science and Technology (KAUST) and the KAUST Office of Sponsored Research ...
doi:10.1101/722918
fatcat:t7lob3q2fndmxngn4udvqxr7ea
Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review
2015
Neuroinformatics
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White matter hyperintensities (WMH) are commonly seen in the brain of healthy elderly subjects and patients with several neurological and vascular disorders. ...
A truly reliable and fully automated method for quantitative assessment of WMH on magnetic resonance imaging (MRI) has not yet been identified. ...
Acknowledgments Financial Disclosures/Conflict of interest concerning the research related to the manuscript and the previous 12 months: The authors have no conflict of interest to disclose. ...
doi:10.1007/s12021-015-9260-y
pmid:25649877
pmcid:PMC4468799
fatcat:ce7pdxj5qrepdldsf6gpf6zdyy
A survey on deep learning in medical image analysis
2017
Medical Image Analysis
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2017 pre-print arXiv:1702.05747v1 fatcat:zdedepkenraglidf7b4ojxcohiOther Versions
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. ...
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. ...
ArXiv was searched for papers mentioning one of a set of terms related to medical imaging. ...
doi:10.1016/j.media.2017.07.005
pmid:28778026
fatcat:esbj72ftwvbgzh6jgw367k73j4
AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics
[article]
2022
arXiv
pre-print
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AI-based detection searches the image space to find the regions of interest based on patterns and features. ...
The extraction of minable information from images gives way to the field of radiomics and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. ...
Acknowledgements This project was in part supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2019-06467, and the Canadian Institutes of Health Research ...
arXiv:2110.10332v4
fatcat:vmpxhoolarbrve5ddyfn5umfim
Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images
[article]
2019
arXiv
pre-print
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With the emergence of digital pathology, searching for similar images in large archives has gained considerable attention. ...
The most impressive characteristic of Yottixel is its ability to represent whole slide images (WSIs) in a compact manner. ...
Core research was also supported by NSERC (Natural Sciences and Engineering Research Council of Canada). ...
arXiv:1911.08748v1
fatcat:sp43yj44ezddjktrgdxgnn3kry
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