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Multi-scale semi-supervised clustering of brain images: deriving disease subtypes
[article]
2021
bioRxiv
pre-print
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An additional limitation of both unsupervised and semi-supervised conventional machine learning methods is that they typically model, learn and infer from data at a basis of feature sets pre- defined at ...
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 ...
It generates an interpretable and spatially adaptive multiscale representation via opNMF, which drives semi-supervised clustering. The schematic diagram of MAGIC is shown in Fig. 1. Figure 1 . ...
doi:10.1101/2021.04.19.440501
fatcat:q7it24mssfcg7dsyp6kt6wha4a
Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns
[article]
2022
arXiv
pre-print
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To address the aforementioned limitations, herein, we propose a novel method, termed Surreal-GAN (Semi-SUpeRvised ReprEsentAtion Learning via GAN). ...
a continuously dimensional representation, and infers the disease severity of patients at individual level along each dimension. ...
(Fig. 1a ) To address the aforementioned limitations, we propose a novel method, Surreal-GAN (Semi-Supervised Representation Learning via GAN), for deriving heterogeneity-refined imaging signatures. ...
arXiv:2205.04523v1
fatcat:i2go5t3dcve5vdrtgygedvmiim
Deep representation learning of electronic health records to unlock patient stratification at scale
2020
npj Digital Medicine
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Here we present an unsupervised framework based on deep learning to process heterogeneous EHRs and derive patient representations that can efficiently and effectively enable patient stratification at scale ...
We introduce a representation learning model based on word embeddings, convolutional neural networks, and autoencoders (i.e., ConvAE) to transform patient trajectories into low-dimensional latent vectors ...
ACKNOWLEDGEMENTS R.M. would like to thank the support from the Hasso Plattner Foundation, the Alzheimer's Drug Discovery Foundation and a courtesy GPU donation from Nvidia. ...
doi:10.1038/s41746-020-0301-z
pmid:32699826
pmcid:PMC7367859
fatcat:ddt7xa36jvbzzdkpirhdslxnty
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale
[article]
2020
arXiv
pre-print
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Here, we present a novel unsupervised framework based on deep learning to process heterogeneous EHRs and derive patient representations that can efficiently and effectively enable patient stratification ...
We introduce a representation learning model based on word embeddings, convolutional neural networks and autoencoders (i.e., "ConvAE") to transform patient trajectories into low-dimensional latent vectors ...
neuroimaging, e.g., MRI/PET brain. ...
arXiv:2003.06516v1
fatcat:l2mg5ox6orbi3e7tnlhmbiuesu
The sensitivity of diffusion MRI to microstructural properties and experimental factors
2020
Journal of Neuroscience Methods
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Diffusion MRI is a non-invasive technique to study brain microstructure. ...
This work reviews the state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. ...
., 2019) and deep learning-based algorithms (Nath et al., , 2019 . ...
doi:10.1016/j.jneumeth.2020.108951
pmid:33017644
pmcid:PMC7762827
fatcat:oi3x6utivvh6pbqalmbg3zr5tm
Algorithm Fairness in AI for Medicine and Healthcare
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2110.00603v1 fatcat:3orkrag2jbbt3pau4i6qyammyy
Lastly, we also review emerging technology for mitigating bias via federated learning, disentanglement, and model explainability, and their role in AI-SaMD development. ...
In this perspective article, we summarize the intersectional field of fairness in machine learning through the context of current issues in healthcare, outline how algorithmic biases (e.g. - image acquisition ...
In FedDis, disentanglement was used to disentangle shape and apperance features in brain MRI scans, with only the shape parameter shared between clients (disentangled representation, colored orange) 209 ...
arXiv:2110.00603v2
fatcat:pspb6bqqxjh45an5mhqohysswu
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
[article]
2019
arXiv
pre-print
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This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. ...
Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. ...
Brain For the anatomic region of brain, Jyoti et al. [56] employed a CNN for the detection of Alzheimer's Disease (AD) using the MRI images of OASIS data set [57] . ...
arXiv:1902.05655v1
fatcat:mjplenjrprgavmy5ssniji4cam
Table of Contents
2020
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
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Preclinical Alzheimer's Disease, pp. 572-576. ...
Ashrafi, Mahnaz (University of Tehran), Soltanian-Zadeh, Hamid
(University of Tehran)
16:00-17:30
SuPbPo-01.4
Semi-Supervised Brain Lesion Segmentation Using Training Images
with and without Lesions ...
doi:10.1109/isbi45749.2020.9098467
fatcat:6kxbkb2s5bdc5cmjvxjhotccay
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
2019
IEEE Access
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This paper does not assume prior knowledge of deep learning and makes a significant contribution in explaining the core deep learning concepts to the non-experts in the Medical Community. ...
This paper provides a unique computer vision/machine learning perspective taken on the advances of deep learning in medical imaging. ...
[61] employed a CNN for the detection of Alzheimer's Disease (AD) using the MRI images of OASIS data set [62] . ...
doi:10.1109/access.2019.2929365
fatcat:arimcbjaxrd3zcsjyzd7abjgd4
AI and Medical Imaging Informatics: Current Challenges and Future Directions
2020
IEEE journal of biomedical and health informatics
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It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already ...
This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice ...
However, we note that use of supervision via training exemplars as a signal could be limited and may not fully realize the potential of deep learning.
D. ...
doi:10.1109/jbhi.2020.2991043
pmid:32609615
pmcid:PMC8580417
fatcat:dcaefxwwqjfwla5asin34x2hxm
2020 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 31
2020
IEEE Transactions on Neural Networks and Learning Systems
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The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. ...
Diseases Leveraging Coupled Interaction for Multimodal Alzheimer's Disease Diagnosis. ...
doi:10.1109/tnnls.2020.3045307
fatcat:34qoykdtarewhdscxqj5jvovqy
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia
2016
NeuroImage
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Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data ...
We hypothesized that the lower-tohigher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the ...
BK21 plus program of the NRF of Korea, in part by a grant from the Korean Health Technology R&D Project, Ministry of Health & Welfare, Korea (HI12C1847), and in part by National Institutes of Health ( ...
doi:10.1016/j.neuroimage.2015.05.018
pmid:25987366
pmcid:PMC4644699
fatcat:qttutmjktzgc7gdk7r5ev7k5ve
Challenges for biophysical modeling of microstructure
2020
Journal of Neuroscience Methods
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techniques, the development of a generalized standard model for any brain region and pathology, and the seamless communication between different parties involved in the development and application of ...
We build on examples from tumors, ischemia and demyelinating diseases. We then discuss the challenges associated with clinical translation and added value. ...
EP/N018702/1 and the UKRI Future Leaders Fellowship grant no. MR/T020296/1 (to MP), and the National Institutes of Health grants no. R01EB017230 and T32EB001628 (to KGS). ...
doi:10.1016/j.jneumeth.2020.108861
pmid:32692999
fatcat:bapfiafyqvcbtky3qggqibefsu
Graph Representation Learning in Biomedicine
[article]
2021
arXiv
pre-print
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With the remarkable success of representation learning in providing powerful predictions and insights, we have witnessed a rapid expansion of representation learning techniques into modeling, analyzing ...
Areas of profound impact include identifying variants underlying complex traits, disentangling behaviors of single cells and their effects on health, assisting in diagnosis and treatment of patients, and ...
Acknowledgements We gratefully acknowledge the support of NSF under nos. ...
arXiv:2104.04883v2
fatcat:7raztbocfngm3pv57l2iwadgre
Deep Learning in Science
[article]
2020
arXiv
pre-print
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Much of the recent success of Artificial Intelligence (AI) has been spurred on by impressive achievements within a broader family of machine learning methods, commonly referred to as Deep Learning (DL) ...
This paper provides insights on the diffusion and impact of DL in science. ...
Hence, each word is represented by a D-dimensional continuous vectori.e., the word representation. ...
arXiv:2009.01575v2
fatcat:4ttqgjdjfjbydp7flnhcgg5p7m
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