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Multi-scale semi-supervised clustering of brain images: deriving disease subtypes [article]

Junhao WEN, Erdem Varol, Aristeidis Sotiras, Zhijian Yang, Ganesh B. Chand, Guray Erus, Haochang Shou, Gyujoon Hwang, Christos Davatzikos
2021 bioRxiv   pre-print
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]

Zhijian Yang, Junhao Wen, Christos Davatzikos
2022 arXiv   pre-print
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

Isotta Landi, Benjamin S. Glicksberg, Hao-Chih Lee, Sarah Cherng, Giulia Landi, Matteo Danieletto, Joel T. Dudley, Cesare Furlanello, Riccardo Miotto
2020 npj Digital Medicine  
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]

Isotta Landi , Hao-Chih Lee, Matteo Danieletto, Cesare Furlanello (1 and 7), and Riccardo Miotto Bruno Kessler Foundation, Trento, Italy Hasso Plattner Institute for Digital Health at Mount Sinai, NY, Institute for Next Generation Healthcare, NY, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, Department of Mental Health and Pathological Addiction (+3 others)
2020 arXiv   pre-print
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

Maryam Afzali, Tomasz Pieciak, Sharlene Newman, Eleftherios Garifallidis, Evren Özarslan, Hu Cheng, Derek K Jones
2020 Journal of Neuroscience Methods  
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]

Richard J. Chen, Tiffany Y. Chen, Jana Lipkova, Judy J. Wang, Drew F.K. Williamson, Ming Y. Lu, Sharifa Sahai, Faisal Mahmood
2022 arXiv   pre-print
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]

Fouzia Altaf, Syed M. S. Islam, Naveed Akhtar, Naeem K. Janjua
2019 arXiv   pre-print
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)  
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

Fouzia Altaf, Syed M S Islam, Naveed Akhtar, Naeem Khalid Janjua
2019 IEEE Access  
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

Andreas S. Panayides, Amir Amini, Nenad Filipovic, Ashish Sharma, Sotirios Tsaftaris, Alistair Young, David J. Foran, Nhan Do, Spyretta Golemati, Tahsin Kurc, Kun Huang, Konstantina S. Nikita (+4 others)
2020 IEEE journal of biomedical and health informatics  
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  
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

Junghoe Kim, Vince D. Calhoun, Eunsoo Shim, Jong-Hwan Lee
2016 NeuroImage  
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

Ileana O. Jelescu, Marco Palombo, Francesca Bagnato, Kurt G. Schilling
2020 Journal of Neuroscience Methods  
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]

Michelle M. Li, Kexin Huang, Marinka Zitnik
2021 arXiv   pre-print
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]

Stefano Bianchini, Moritz Müller, Pierre Pelletier
2020 arXiv   pre-print
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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