research
∙
08/09/2023
Expert load matters: operating networks at high accuracy and low manual effort
In human-AI collaboration systems for critical applications, in order to...
share
research
∙
08/03/2023
Quantification of Predictive Uncertainty via Inference-Time Sampling
Predictive variability due to data ambiguities has typically been addres...
research
∙
04/12/2023
Explicitly Minimizing the Blur Error of Variational Autoencoders
Variational autoencoders (VAEs) are powerful generative modelling method...
research
∙
03/31/2023
Live image-based neurosurgical guidance and roadmap generation using unsupervised embedding
Advanced minimally invasive neurosurgery navigation relies mainly on Mag...
research
∙
02/20/2023
InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation
3D object recognition has successfully become an appealing research topi...
research
∙
01/30/2023
FedFA: Federated Feature Augmentation
Federated learning is a distributed paradigm that allows multiple partie...
research
∙
11/28/2022
Unsupervised Superpixel Generation using Edge-Sparse Embedding
Partitioning an image into superpixels based on the similarity of pixels...
research
∙
04/13/2022
Neural Vector Fields for Surface Representation and Inference
Neural implicit fields have recently been shown to represent 3D shapes a...
research
∙
03/28/2022
Rethinking Semantic Segmentation: A Prototype View
Prevalent semantic segmentation solutions, despite their different netwo...
research
∙
03/16/2022
Zero Pixel Directional Boundary by Vector Transform
Boundaries are among the primary visual cues used by human and computer ...
research
∙
02/10/2022
A Field of Experts Prior for Adapting Neural Networks at Test Time
Performance of convolutional neural networks (CNNs) in image analysis ta...
research
∙
12/19/2021
Wiener Guided DIP for Unsupervised Blind Image Deconvolution
Blind deconvolution is an ill-posed problem arising in various fields ra...
research
∙
12/17/2021
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
Supervised deep learning-based methods yield accurate results for medica...
research
∙
11/27/2021
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior
Recent works show that convolutional neural network (CNN) architectures ...
research
∙
06/20/2021
Quality-Aware Memory Network for Interactive Volumetric Image Segmentation
Despite recent progress of automatic medical image segmentation techniqu...
research
∙
05/11/2021
Gradient flow encoding with distance optimization adaptive step size
The autoencoder model uses an encoder to map data samples to a lower dim...
research
∙
03/30/2021
Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classification
Learning robust representations to discriminate cell phenotypes based on...
research
∙
02/21/2021
Constrained Optimization for Training Deep Neural Networks Under Class Imbalance
Deep neural networks (DNNs) are notorious for making more mistakes for t...
research
∙
01/28/2021
Exploring Cross-Image Pixel Contrast for Semantic Segmentation
Current semantic segmentation methods focus only on mining "local" conte...
research
∙
01/19/2021
Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets
This work studies Hyperspectral image (HSI) super-resolution (SR). HSI S...
research
∙
10/05/2020
Probabilistic 3D surface reconstruction from sparse MRI information
Surface reconstruction from magnetic resonance (MR) imaging data is indi...
research
∙
09/30/2020
Sampling possible reconstructions of undersampled acquisitions in MR imaging
Undersampling the k-space during MR acquisitions saves time, however res...
research
∙
08/16/2020
RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation
Quantifying segmentation uncertainty has become an important issue in me...
research
∙
07/26/2020
Joint reconstruction and bias field correction for undersampled MR imaging
Undersampling the k-space in MRI allows saving precious acquisition time...
research
∙
07/09/2020
Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
Supervised learning-based segmentation methods typically require a large...
research
∙
07/09/2020
Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE
Probabilistic modelling has been an essential tool in medical image anal...
research
∙
06/18/2020
Unsupervised out-of-distribution detection using kernel density estimation
Deep neural networks achieve significant advancement to the state-of-the...
research
∙
06/18/2020
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...
research
∙
04/30/2020
Unsupervised Lesion Detection via Image Restoration with a Normative Prior
Unsupervised lesion detection is a challenging problem that requires acc...
research
∙
04/09/2020
Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
Convolutional Neural Networks (CNNs) work very well for supervised learn...
research
∙
01/31/2020
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...
research
∙
10/10/2019
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 ...
research
∙
06/14/2019
A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
One of the key drawbacks of 3D convolutional neural networks for segment...
research
∙
06/07/2019
PHiSeg: Capturing Uncertainty in Medical Image Segmentation
Segmentation of anatomical structures and pathologies is inherently ambi...
research
∙
02/20/2019
Adversarial Augmentation for Enhancing Classification of Mammography Images
Supervised deep learning relies on the assumption that enough training d...
research
∙
02/11/2019
Semi-Supervised and Task-Driven Data Augmentation
Supervised deep learning methods for segmentation require large amounts ...
research
∙
11/19/2018
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...
research
∙
09/27/2018
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning
Revealing latent structure in data is an active field of research, havin...
research
∙
07/30/2018
Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors
Surface reconstruction is a vital tool in a wide range of areas of medic...
research
∙
07/24/2018
Combining Heterogeneously Labeled Datasets For Training Segmentation Networks
Accurate segmentation of medical images is an important step towards ana...
research
∙
07/23/2018
Iterative Interaction Training for Segmentation Editing Networks
Automatic segmentation has great potential to facilitate morphological m...
research
∙
07/19/2018
Generative Adversarial Networks for MR-CT Deformable Image Registration
Deformable Image Registration (DIR) of MR and CT images is one of the mo...
research
∙
07/12/2018
Learning to Segment Medical Images with Scribble-Supervision Alone
Semantic segmentation of medical images is a crucial step for the quanti...
research
∙
06/14/2018
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging
Recent advances in deep learning led to novel generative modeling techni...
research
∙
06/13/2018
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders
Lesion detection in brain Magnetic Resonance Images (MRI) remains a chal...
research
∙
05/25/2018
A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols
Convolutional neural networks (CNNs) have shown promising results on sev...
research
∙
04/12/2018
Temporal Interpolation via Motion Field Prediction
Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables...
research
∙
11/30/2017
MR image reconstruction using the learned data distribution as prior
MR image reconstruction from undersampled data exploits priors to compen...
research
∙
11/24/2017
Visual Feature Attribution using Wasserstein GANs
Attributing the pixels of an input image to a certain category is an imp...
research
∙
09/13/2017