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Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging [article]

Vishnu M. Bashyam, Jimit Doshi, Guray Erus, Dhivya Srinivasan, Ahmed Abdulkadir, Mohamad Habes, Yong Fan, Colin L. Masters, Paul Maruff, Chuanjun Zhuo, Henry Völzke, Sterling C. Johnson (+15 others)
<span title="2020-10-11">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
By learning an unsupervised image to image canonical mapping from diverse datasets to a reference domain using generative deep learning models, we aim to reduce confounding data variation while preserving  ...  Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and by contributing to precision  ...  For testing, the median prediction (prediction probability for classification) of a scan is used.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.05355v1">arXiv:2010.05355v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d46qextpzndehadrcmcuwaaaxy">fatcat:d46qextpzndehadrcmcuwaaaxy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201025204248/https://arxiv.org/ftp/arxiv/papers/2010/2010.05355.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6d/ec/6decac1287a6d8c473ffcb0048fcf54d0c54f006.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.05355v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Harmonization with Flow-based Causal Inference [article]

Rongguang Wang, Pratik Chaudhari, Christos Davatzikos
<span title="2021-07-10">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Heterogeneity in medical data, e.g., from data collected at different sites and with different protocols in a clinical study, is a fundamental hurdle for accurate prediction using machine learning models  ...  A causal model is used to model observed effects (brain magnetic resonance imaging data) that result from known confounders (site, gender and age) and exogenous noise variables.  ...  Pratik Chaudhari would like to acknowledge the support of the Amazon Web Services Machine Learning Research Award. References  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.06845v2">arXiv:2106.06845v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hildildhc5go3fojq3rjtcjfuy">fatcat:hildildhc5go3fojq3rjtcjfuy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210718215554/https://arxiv.org/pdf/2106.06845v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3a/e1/3ae1ed9a3cef8967ed60b2a1fe108b2c6edc6aae.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.06845v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

2020 Index IEEE Transactions on Image Processing Vol. 29

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dhlhr4jqkbcmdbua2ca45o7kru" style="color: black;">IEEE Transactions on Image Processing</a> </i> &nbsp;
., +, TIP 2020 538-550 Semi-Supervised Robust Mixture Models in RKHS for Abnormality Detection in Medical Images.  ...  ., +, TIP 2020 9204-9219 Semi-Supervised Robust Mixture Models in RKHS for Abnormality Detec- tion in Medical Images.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tip.2020.3046056">doi:10.1109/tip.2020.3046056</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/24m6k2elprf2nfmucbjzhvzk3m">fatcat:24m6k2elprf2nfmucbjzhvzk3m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201224144031/https://ieeexplore.ieee.org/ielx7/83/8835130/09301460.pdf?tp=&amp;arnumber=9301460&amp;isnumber=8835130&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/56/93/5693eebc307c33915511489f6dcddcb127981534.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tip.2020.3046056"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks

Sulaiman Vesal, Andreas Maier, Nishant Ravikumar
<span title="2020-07-06">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/au3ye363lzbdroopx7rzfyv63m" style="color: black;">Journal of Imaging</a> </i> &nbsp;
Deep learning approaches based on 3D fully convolutional networks (FCNs), have improved state-of-the-art segmentation performance in CMR images.  ...  The 3D U-Net with some architectural changes (referred to as 3D DR-UNet) was used as the base architecture in this framework for both the multi-stage and end-to-end strategies.  ...  Recently, there have been tremendous improvements in cardiac MRI segmentation [15] , and in medical image segmentation in general, using deep convolutional network architectures [16, 17] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/jimaging6070065">doi:10.3390/jimaging6070065</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34460658">pmid:34460658</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8321054/">pmcid:PMC8321054</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aaqtmncrgbhcpdcekmzoulfr5q">fatcat:aaqtmncrgbhcpdcekmzoulfr5q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200723110411/https://res.mdpi.com/d_attachment/jimaging/jimaging-06-00065/article_deploy/jimaging-06-00065-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/00/a3/00a3e0d11c7f783a5598a5e44986c5cdfc1fed40.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/jimaging6070065"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321054" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

From multivariate methods to an AI ecosystem

Nils R. Winter, Micah Cearns, Scott R. Clark, Ramona Leenings, Udo Dannlowski, Bernhard T. Baune, Tim Hahn
<span title="2021-05-12">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ywuy2l3c7ngernsmmsbttqvzna" style="color: black;">Molecular Psychiatry</a> </i> &nbsp;
Explaining the translational roadblock In the social and medical sciences, in light of the recent replication crisis, a failure to replicate results has rendered many discoveries from group-based statistical  ...  For example, at a recent international machine learning competition, participants sought to classify major depressive disorder (MDD) patients from healthy controls using structural Magnetic Resonance Imaging  ...  Acknowledgements This work was funded by the German Research Foundation (DFG grants HA7070/2-2, HA7070/3, HA7070/4 to TH) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41380-021-01116-y">doi:10.1038/s41380-021-01116-y</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33981009">pmid:33981009</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8760040/">pmcid:PMC8760040</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hspc2ldl5zblzhx3kcmzcqn5jm">fatcat:hspc2ldl5zblzhx3kcmzcqn5jm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210718161706/https://www.nature.com/articles/s41380-021-01116-y.pdf?error=cookies_not_supported&amp;code=05fa73c5-919c-4e24-90c3-ce896cbf4dc5" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a1/83/a1836bb5c0ba33ff11c39d204b6a9dd7cc8644c7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41380-021-01116-y"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8760040" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Training a Neural Network for Gibbs and Noise Removal in Diffusion MRI [article]

Matthew J. Muckley, Benjamin Ades-Aron, Antonios Papaioannou, Gregory Lemberskiy, Eddy Solomon, Yvonne W. Lui, Daniel K. Sodickson, Els Fieremans, Dmitry S. Novikov, Florian Knoll
<span title="2019-05-15">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps.  ...  The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.  ...  The raw images (Raw) served as the input for the magnitude deep learning (MCNN) and complex deep learning (CCNN) methods.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.04176v2">arXiv:1905.04176v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ebtwn7mfm5asbasdxrcjkxxlji">fatcat:ebtwn7mfm5asbasdxrcjkxxlji</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200905160702/https://arxiv.org/pdf/1905.04176v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2d/cc/2dcc97a3f167e03d7cb79be1aa2517df417d5fae.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.04176v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review

Ye-Jiao Mao, Hyo-Jung Lim, Ming Ni, Wai-Hin Yan, Duo Wai-Chi Wong, James Chung-Wai Cheung
<span title="2022-01-12">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2zwku6u6nfdcri773tpisi6ldi" style="color: black;">Cancers</a> </i> &nbsp;
Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks  ...  Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.  ...  Acknowledgments: Icons of the graphical abstract were royalty-free and extracted from Flaticon (https://www.flaticon.com/, accessed on 20 December 2021) and Freepik (https://www.freepik. com/, accessed  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/cancers14020367">doi:10.3390/cancers14020367</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35053531">pmid:35053531</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8773731/">pmcid:PMC8773731</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iagxt7ctrnebtovrxll3tytu3q">fatcat:iagxt7ctrnebtovrxll3tytu3q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220429184841/https://mdpi-res.com/d_attachment/cancers/cancers-14-00367/article_deploy/cancers-14-00367.pdf?version=1642071328" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/37/03/37034b532c0e7d81d6525a18ec0e10eb522fa683.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/cancers14020367"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8773731" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Towards Label-Free 3D Segmentation of Optical Coherence Tomography Images of the Optic Nerve Head Using Deep Learning [article]

Sripad Krishna Devalla, Tan Hung Pham, Satish Kumar Panda, Liang Zhang, Giridhar Subramanian, Anirudh Swaminathan, Chin Zhi Yun, Mohan Rajan, Sujatha Mohan, Ramaswami Krishnadas, Vijayalakshmi Senthil, John Mark S. de Leon, Tin A. Tun (+6 others)
<span title="2020-02-22">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The first (referred to as the enhancer) was able to enhance OCT image quality from 3 OCT devices, and harmonized image-characteristics across these devices.  ...  Although several deep learning (DL) techniques have been recently proposed for the automated extraction (segmentation) and quantification of these morphological changes, the device specific nature and  ...  Specifically, ensemble learning has shown to better generalize and increase the robustness of segmentations in OCT [69, 18] and other medical imaging modalities [31, 45, 52, 95] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.09635v1">arXiv:2002.09635v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mr5wfnfhc5hc7i443xfc5kiosu">fatcat:mr5wfnfhc5hc7i443xfc5kiosu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321115522/https://arxiv.org/pdf/2002.09635v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.09635v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges

James T. T. Coates, Giacomo Pirovano, Issam El Naqa
<span title="2021-03-23">2021</span> <i title="SPIE-Intl Soc Optical Eng"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/j3czr3qjj5cqnfkjzwhi6ttvay" style="color: black;">Journal of Medical Imaging</a> </i> &nbsp;
Discussion then focuses on uses of conventional and deep machine learning in radiomics.  ...  factors together with treatment and diagnostic information to generate individualized patient risk profiles, and radiomics, which further leverages large-scale imaging correlates and extracted features  ...  of deep learning algorithms in the medical sciences.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/1.jmi.8.3.031902">doi:10.1117/1.jmi.8.3.031902</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33768134">pmid:33768134</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7985651/">pmcid:PMC7985651</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/y4djrrysrbbifcm5dz6gbjsumu">fatcat:y4djrrysrbbifcm5dz6gbjsumu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210716103020/https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-8/issue-3/031902/Radiomic-and-radiogenomic-modeling-for-radiotherapy--strategies-pitfalls-and/10.1117/1.JMI.8.3.031902.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/62/66/6266fa5d1ddb0a5a181906f9e13179c72b2ef47c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/1.jmi.8.3.031902"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7985651" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving [article]

Junyao Guo, Unmesh Kurup, Mohak Shah
<span title="2018-11-27">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The machine learning algorithms that currently do so learn predominantly in a supervised manner and consequently need sufficient data for robust and efficient learning.  ...  Specifically, we categorize the datasets according to use cases, and highlight the datasets that capture complicated and hazardous driving conditions which can be better used for training robust driving  ...  end-to-end learning model, where [1] uses affine image transformations and [32] uses GAN to generate the test images.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1811.11277v1">arXiv:1811.11277v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ztrxyydtuveijizfn6a2dmt5ui">fatcat:ztrxyydtuveijizfn6a2dmt5ui</a> </span>
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Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, J. Santamaría, Mohammed A. Fadhel, Muthana Al-Amidie, Laith Farhan
<span title="2021-03-31">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pkhnkszyprhb3orbf6g7tqmgiu" style="color: black;">Journal of Big Data</a> </i> &nbsp;
AbstractIn the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community.  ...  More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing  ...  Acknowledgements We would like to thank the professors from the Queensland University of Technology and the University of Information Technology and Communications who gave their feedback on the paper.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s40537-021-00444-8">doi:10.1186/s40537-021-00444-8</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33816053">pmid:33816053</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8010506/">pmcid:PMC8010506</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x2h5qs5c2jbntipu7oi5hfnb6u">fatcat:x2h5qs5c2jbntipu7oi5hfnb6u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210401042044/https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-021-00444-8.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/86/4d/864dea14fc4c893d8b79fc7fa0dd2a350bfaf73b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s40537-021-00444-8"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010506" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
<span title="2021-01-10">2021</span> <i title="IEEE"> 2020 25th International Conference on Pattern Recognition (ICPR) </i> &nbsp;
of SaltMarsh Plants from Images Using Deep Learning Emerging Relation Network and Task Embedding for Multi-Task Regression Problems.pdf Learning from Learners: Adapting Reinforcement Learning Agents to  ...  Human Embryo Cell Centroid Localization and Counting in Time- Lapse Sequences DAY 1 -Jan 12, 2021 Guo, Danfeng; Terzopoulos, Demetri 2432 A Transformer-Based Network forAnisotropic 3D Medical Image  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icpr48806.2021.9412725">doi:10.1109/icpr48806.2021.9412725</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3vge2tpd2zf7jcv5btcixnaikm">fatcat:3vge2tpd2zf7jcv5btcixnaikm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210717011531/https://opus.lib.uts.edu.au/bitstream/10453/147247/4/icpr-2020-conference-program-final.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c6/ae/c6ae1be81aac323ae2502c86b31c786b9b2567dd.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icpr48806.2021.9412725"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Technical Challenges and Solutions [article]

Eike Petersen, Yannik Potdevin, Esfandiar Mohammadi, Stephan Zidowitz, Sabrina Breyer, Dirk Nowotka, Sandra Henn, Ludwig Pechmann, Martin Leucker, Philipp Rostalski, Christian Herzog
<span title="2021-07-20">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Machine learning is expected to fuel significant improvements in medical care.  ...  In this paper, we survey the technical challenges involved in creating medical machine learning systems responsibly and in conformity with existing regulations, as well as possible solutions to address  ...  (BMWi) and involves several universities, research institutions and private companies in northern Germany.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.09546v1">arXiv:2107.09546v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/er3nlre7xrg4lmqsgxs7c4pswu">fatcat:er3nlre7xrg4lmqsgxs7c4pswu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210723024732/https://arxiv.org/pdf/2107.09546v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f5/9a/f59a9350b235bf7b6b778821e94c39eb6a3ea3ee.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.09546v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Robust and Efficient Medical Imaging with Self-Supervision [article]

Shekoofeh Azizi, Laura Culp, Jan Freyberg, Basil Mustafa, Sebastien Baur, Simon Kornblith, Ting Chen, Patricia MacWilliams, S. Sara Mahdavi, Ellery Wulczyn, Boris Babenko, Megan Wilson (+22 others)
<span title="2022-05-19">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To meet these challenges, we present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI.  ...  We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.  ...  The images and data used in this publication are derived from the Optimam database the creation of which was funded by Cancer Research UK.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.09723v1">arXiv:2205.09723v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vilfmgocyzbuhb3ilwtdovdr74">fatcat:vilfmgocyzbuhb3ilwtdovdr74</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220524054432/https://arxiv.org/pdf/2205.09723v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/48/b0/48b0e0624e3251ae7af29aa4db22e2bb1886a1fb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.09723v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

3D Morphable Face Models – Past, Present and Future [article]

Bernhard Egger, William A. P. Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani, Christian Theobalt, Volker Blanz (+1 others)
<span title="2020-04-16">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The challenges in building and applying these models, namely capture, modeling, image formation, and image analysis, are still active research topics, and we review the state-of-the-art in each of these  ...  In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed.  ...  This survey paper was partially funded by Early PostDoc Mobility Grant, Swiss National Science Foundation P2BSP2_178643, ERC Consolidator Grant 4DRepLy and the Max Planck Center for Visual Computing and  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.01815v2">arXiv:1909.01815v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/g3wtdppy7ze3dildi6a3siq4qy">fatcat:g3wtdppy7ze3dildi6a3siq4qy</a> </span>
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