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20172022
most citedTraining recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling

142 citations · 216 across the 6 of their papers we have counts for

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8 papers · 1 filter

cs.CV2019★ 142 cited

Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling

Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai +4

Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make pa…

cs.CV2018

PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation

Mauricio Orbes Arteaga, Lauge Sørensen, M. Jorge Cardoso +6

For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear sha…

cs.CV2018

Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs

Mauricio Orbes-Arteaga, M. Jorge Cardoso, Lauge Sørensen +4

Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on whi…

cs.CV2018

Robust training of recurrent neural networks to handle missing data for disease progression modeling

Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai +4

Disease progression modeling (DPM) using longitudinal data is a challenging task in machine learning for healthcare that can provide clinicians with better tools for diagnosis and…

cs.CV2017

A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images

Akshay Pai, Stefan Sommer, Lars Lau Raket +4

Template estimation plays a crucial role in computational anatomy since it provides reference frames for performing statistical analysis of the underlying anatomical population var…

cs.CV2017★ 30 cited

Label Stability in Multiple Instance Learning

Veronika Cheplygina, Lauge Sørensen, David M. J. Tax +2

We address the problem of \emph{instance label stability} in multiple instance learning (MIL) classifiers. These classifiers are trained only on globally annotated images (bags), b…