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Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
“L1 approximation and the analysis of data”
Ian Barrodale · 1968
Earlier work this paper cites.
“The hadamard product”
Roger Horn · 1990
Earlier work this paper cites.
“Morphometric analysis of white matter lesions in MR images: method and validation”
Alex Zijdenbos, Benoit Dawant, Richard Margolin and Andrew Palmer · 1994
Earlier work this paper cites.
“HIPAA regulations-a new era of medical-record privacy?”
George Annas · 2003
Earlier work this paper cites.
“User-Guided 3D Active Contour Segmentation of Anatomical Structures: Significantly Improved Efficiency and Reliability”
Paul. Yushkevich et al · 2006
Earlier work this paper cites.
“Clinical research for rare disease: opportunities, challenges, and solutions”
Robert Griggs et al · 2009
Earlier work this paper cites.
“Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1”
Roel Verhaak et al · 2010
Earlier work this paper cites.
“The somatic genomic landscape of glioblastoma”
Cameron Brennan et al · 2013
Earlier work this paper cites.
“Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics”
Andrea Sottoriva et al · 2013
Earlier work this paper cites.
“Multi-institutional validation of a preoperative scoring system which predicts survival for patients with glioblastoma”
Kaisorn Chaichana et al · 2013
Earlier work this paper cites.
“RTOG 0825: Phase III double-blind placebo-controlled trial evaluating bevacizumab (Bev) in patients (Pts) with newly diagnosed glioblastoma (GBM).”
Mark Gilbert et al · 2013
Earlier work this paper cites.
“Enhanced precision analysis for accuracy-aware bit-width optimization using affine arithmetic”
Shervin Vakili, JM Langlois and Guy Bois · 2013
Earlier work this paper cites.
“Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity”
Hamed Akbari et al · 2014
Earlier work this paper cites.
“The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data”
Paul Thompson et al · 2014
Earlier work this paper cites.
“A randomized trial of bevacizumab for newly diagnosed glioblastoma”
Mark Gilbert et al · 2014
Earlier work this paper cites.
“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
“The untapped potential of trusted execution environments on mobile devices”
Jan-Erik Ekberg, Kari Kostiainen and N Asokan · 2014
Earlier work this paper cites.
“GLISTRboost: combining multimodal MRI segmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation”
Spyridon Bakas et al · 2015
Earlier work this paper cites.
“Segmentation of gliomas in multimodal magnetic resonance imaging volumes based on a hybrid generative-discriminative framework”
Spyridon Bakas et al · 2015
Earlier work this paper cites.
“U-net: Convolutional networks for biomedical image segmentation”
Olaf Ronneberger, Philipp Fischer and Thomas Brox · 2015
Earlier work this paper cites.
“Imaging surrogates of infiltration obtained via multiparametric imaging pattern analysis predict subsequent location of recurrence of glioblastoma”
Hamed Akbari et al · 2016
Earlier work this paper cites.
“Predicting the future—big data, machine learning, and clinical medicine”
Ziad Obermeyer and Ezekiel Emanuel · 2016
Earlier work this paper cites.
“Segmentation of gliomas in pre-operative and post-operative multimodal magnetic resonance imaging volumes based on a hybrid generative-discriminative framework”
Ke Zeng et al · 2016
Earlier work this paper cites.
“3D U-Net: learning dense volumetric segmentation from sparse annotation”
Özgün Çiçek et al · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“The importance of skip connections in biomedical image segmentation”
Michal Drozdzal et al · 2016
Earlier work this paper cites.
“Efficient and robust deep learning with correntropy-induced loss function”
Liangjun Chen et al · 2016
Earlier work this paper cites.
“Fixed point quantization of deep convolutional networks”
Darryl Lin, Sachin Talathi and Sreekanth Annapureddy · 2016
Earlier work this paper cites.
“The FAIR Guiding Principles for scientific data management and stewardship”
Mark Wilkinson et al · 2016
Earlier work this paper cites.
“Fast automatic segmentation of hippocampal subfields and medial temporal lobe subregions in 3 Tesla and 7 Tesla T2-weighted MRI”
Paul Yushkevich et al · 2016
Earlier work this paper cites.
“In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the φ \varphi -index”
Spyridon Bakas et al · 2017
Earlier work this paper cites.
“The eu general data protection regulation (gdpr)”
Paul Voigt and Axel Von · 2017
Earlier work this paper cites.
“Communication-efficient learning of deep networks from decentralized data”
Brendan McMahan et al · 2017
Earlier work this paper cites.
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Gaurav Shukla et al · 2017
Cited alongside, same era.
“Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features”
Spyridon Bakas et al · 2017
Cited alongside, same era.
“Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations”
Carole Sudre et al · 2017
Cited alongside, same era.
“Tversky loss function for image segmentation using 3D fully convolutional deep networks”
Seyed Salehi, Deniz Erdogmus and Ali Gholipour · 2017
Cited alongside, same era.
“Brain cancer imaging phenomics toolkit (brain-CaPTk): an interactive platform for quantitative analysis of glioblastoma”
Saima Rathore et al · 2017
Cited alongside, same era.
“The cancer imaging phenomics toolkit (captk): Technical overview”
Sarthak Pati et al · 2019
Later among the works it cites.
“Pytorch: An imperative style, high-performance deep learning library”
Adam Paszke et al · 2019
Later among the works it cites.
“OpenVINO deep learning workbench: Comprehensive analysis and tuning of neural networks inference”
Yury Gorbachev et al · 2019
Later among the works it cites.
“Imaging signatures of glioblastoma molecular characteristics: a radiogenomics review”
Anahita Fathi, Spyridon Bakas, Hamidreza Saligheh and Christos Davatzikos · 2020
Later among the works it cites.
“Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI modalities”
Spyridon Bakas et al · 2020
Later among the works it cites.
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Cited alongside, same era.
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Cited alongside, same era.
“Epidermal growth factor receptor extracellular domain mutations in glioblastoma present opportunities for clinical imaging and therapeutic development”
Zev Binder et al · 2018
Cited alongside, same era.
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Cited alongside, same era.
“NIMG-40. Non-invasive in vivo signature of idh1 mutational status in high grade glioma, from clinically-acquired multi-parametric magnetic resonance imaging, using multivariate machine learning”
Spyridon Bakas et al · 2018
Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Later among the works it cites.
“Integrated biophysical modeling and image analysis: application to neuro-oncology”
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Later among the works it cites.
“The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study”
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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W Han et al · 2020
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Later among the works it cites.
“Cancer imaging phenomics via CaPTk: multi-institutional prediction of progression-free survival and pattern of recurrence in glioblastoma”
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Later among the works it cites.
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