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08/08/2023
Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging
Deep learning-based models in medical imaging often struggle to generali...
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01/23/2023
Deep Learning-Based Assessment of Cerebral Microbleeds in COVID-19
Cerebral Microbleeds (CMBs), typically captured as hypointensities from ...
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12/27/2022
Robust Cross-vendor Mammographic Texture Models Using Augmentation-based Domain Adaptation for Long-term Breast Cancer Risk
The future of population-based breast cancer screening is likely persona...
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08/30/2022
FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain
Medical images used in clinical practice are heterogeneous and not the s...
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02/23/2022
Augmentation based unsupervised domain adaptation
The insertion of deep learning in medical image analysis had lead to the...
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06/24/2021
Information Bottleneck: Exact Analysis of (Quantized) Neural Networks
The information bottleneck (IB) principle has been suggested as a way to...
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01/18/2021
Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts
Multimodal generative models should be able to learn a meaningful latent...
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05/20/2020
Lung Segmentation from Chest X-rays using Variational Data Imputation
Pulmonary opacification is the inflammation in the lungs caused by many ...
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02/09/2020
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
We present the findings of "The Alzheimer's Disease Prediction Of Longit...
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12/22/2019
On the Initialization of Long Short-Term Memory Networks
Weight initialization is important for faster convergence and stability ...
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08/16/2019
Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning
Supervised learning algorithms trained on medical images will often fail...
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08/16/2019
Knowledge distillation for semi-supervised domain adaptation
In the absence of sufficient data variation (e.g., scanner and protocol ...
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08/14/2019
Robust parametric modeling of Alzheimer's disease progression
Quantitative characterization of disease progression using longitudinal ...
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03/17/2019
Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
Disease progression modeling (DPM) using longitudinal data is a challeng...
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10/03/2018
PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
For proper generalization performance of convolutional neural networks (...
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09/21/2018
On Variational Methods for Motion Compensated Inpainting
We develop in this paper a generic Bayesian framework for the joint esti...
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08/20/2018
Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
Segmenting vascular pathologies such as white matter lesions in Brain ma...
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08/16/2018
Robust training of recurrent neural networks to handle missing data for disease progression modeling
Disease progression modeling (DPM) using longitudinal data is a challeng...
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04/03/2017
Convolutional neural networks for segmentation and object detection of human semen
We compare a set of convolutional neural network (CNN) architectures for...
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07/23/2012
Towards a theory of statistical tree-shape analysis
In order to develop statistical methods for shapes with a tree-structure...
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12/14/2011