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Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classification
Learning robust representations to discriminate cell phenotypes based on...
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Constrained Optimization for Training Deep Neural Networks Under Class Imbalance
Deep neural networks (DNNs) are notorious for making more mistakes for t...
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Exploring Cross-Image Pixel Contrast for Semantic Segmentation
Current semantic segmentation methods focus only on mining "local" conte...
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Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets
This work studies Hyperspectral image (HSI) super-resolution (SR). HSI S...
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Probabilistic 3D surface reconstruction from sparse MRI information
Surface reconstruction from magnetic resonance (MR) imaging data is indi...
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Sampling possible reconstructions of undersampled acquisitions in MR imaging
Undersampling the k-space during MR acquisitions saves time, however res...
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RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation
Quantifying segmentation uncertainty has become an important issue in me...
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Joint reconstruction and bias field correction for undersampled MR imaging
Undersampling the k-space in MRI allows saving precious acquisition time...
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Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
Supervised learning-based segmentation methods typically require a large...
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Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE
Probabilistic modelling has been an essential tool in medical image anal...
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Unsupervised out-of-distribution detection using kernel density estimation
Deep neural networks achieve significant advancement to the state-of-the...
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Contrastive learning of global and local features for medical image segmentation with limited annotations
A key requirement for the success of supervised deep learning is a large...
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Unsupervised Lesion Detection via Image Restoration with a Normative Prior
Unsupervised lesion detection is a challenging problem that requires acc...
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Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
Convolutional Neural Networks (CNNs) work very well for supervised learn...
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Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with Bayesian inference for uncertainty-based quality-control
Tissue characterisation with CMR parametric mapping has the potential to...
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Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects
This is an empirical study to investigate the impact of scanner effects ...
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A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
One of the key drawbacks of 3D convolutional neural networks for segment...
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PHiSeg: Capturing Uncertainty in Medical Image Segmentation
Segmentation of anatomical structures and pathologies is inherently ambi...
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Adversarial Augmentation for Enhancing Classification of Mammography Images
Supervised deep learning relies on the assumption that enough training d...
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Semi-Supervised and Task-Driven Data Augmentation
Supervised deep learning methods for segmentation require large amounts ...
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Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks
Purpose To train a cycle-consistent generative adversarial network (Cycl...
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Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning
Revealing latent structure in data is an active field of research, havin...
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Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors
Surface reconstruction is a vital tool in a wide range of areas of medic...
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Combining Heterogeneously Labeled Datasets For Training Segmentation Networks
Accurate segmentation of medical images is an important step towards ana...
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Iterative Interaction Training for Segmentation Editing Networks
Automatic segmentation has great potential to facilitate morphological m...
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Generative Adversarial Networks for MR-CT Deformable Image Registration
Deformable Image Registration (DIR) of MR and CT images is one of the mo...
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Learning to Segment Medical Images with Scribble-Supervision Alone
Semantic segmentation of medical images is a crucial step for the quanti...
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Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging
Recent advances in deep learning led to novel generative modeling techni...
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Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders
Lesion detection in brain Magnetic Resonance Images (MRI) remains a chal...
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A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols
Convolutional neural networks (CNNs) have shown promising results on sev...
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Temporal Interpolation via Motion Field Prediction
Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables...
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MR image reconstruction using the learned data distribution as prior
MR image reconstruction from undersampled data exploits priors to compen...
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Visual Feature Attribution using Wasserstein GANs
Attributing the pixels of an input image to a certain category is an imp...
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An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation
Accurate segmentation of the heart is an important step towards evaluati...
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Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots
Ingestible wireless capsule endoscopy is an emerging minimally invasive ...
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A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot
In the gastrointestinal (GI) tract endoscopy field, ingestible wireless ...
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Magnetic-Visual Sensor Fusion based Medical SLAM for Endoscopic Capsule Robot
A reliable, real-time simultaneous localization and mapping (SLAM) metho...
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A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots
In the gastrointestinal (GI) tract endoscopy field, ingestible wireless ...
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A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots
We present a robust deep learning based 6 degrees-of-freedom (DoF) local...
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SubCMap: Subject and Condition Specific Effect Maps
Most widely used statistical analysis methods for neuroimaging data iden...
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WESD - Weighted Spectral Distance for Measuring Shape Dissimilarity
This article presents a new distance for measuring shape dissimilarity b...
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