Search Results for author:
Found 18 papers, 3 papers with code
Multi-view analysis of unregistered medical images using cross-view transformers
We present a novel cross-view transformer method to transfer information between unregistered views at the level of spatial feature maps.
Domain Adaptation with Randomized Expectation Maximization
Despite their success, state of the art methods based on this approach are either involved or unable to directly scale to data with many features.
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting
2) How is the performance of hyperspectral image classification methods affected when using disjoint train and test sets?
Adversarial Alignment of Class Prediction Uncertainties for Domain Adaptation
Under the assumption that features from pre-trained deep neural networks are transferable across related domains, domain adaptation reduces to aligning source and target domain at class prediction uncertainty level.
Unsupervised Domain Adaptation with Random Walks on Target Labelings
Unsupervised Domain Adaptation (DA) is used to automatize the task of labeling data: an unlabeled dataset (target) is annotated using a labeled dataset (source) from a related domain.
Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders
Our results show that: 1) feature learning outperforms handcrafted features; 2) parameter transfer learning is beneficial in longitudinal settings; 3) using LSTM to learn the temporal dynamic of signals enhances the detection rate especially for skewed training data; 4) an ensemble of LSTMs provides more accurate and stable detectors.
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks.
Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin
In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN).
Local Network Community Detection with Continuous Optimization of Conductance and Weighted Kernel K-Means
We investigate the relation of conductance with weighted kernel k-means for a single community, which leads to the introduction of a new objective function, $\sigma$-conductance.
Axioms for graph clustering quality functions
This motivates the derivation of a new family of quality functions, adaptive scale modularity, which does satisfy the proposed axioms.
Resolution-limit-free and local Non-negative Matrix Factorization quality functions for graph clustering
We argue that this is a desirable property, provide conditions under which NMF quality functions are local, and propose a novel class of local probabilistic NMF quality functions for soft graph clustering.
Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes
Then, we propose a novel SM kernel with a dependency structure (SMD) by using cross-convolution between the SM components.
Multitask Gaussian Process with Hierarchical Latent Interactions
The function interaction is modeled by using cross convolution of latent functions.
Multi-Output Convolution Spectral Mixture for Gaussian Processes
The resulting kernel is called Multi-Output Convolution Spectral Mixture (MOCSM) kernel.
Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation
We analyze two transfer learning settings: 1) unsupervised transfer, where Hologic data with soft lesion annotation at pixel level and Siemens unlabelled data are used to annotate images in the latter data; 2) weak supervised transfer, where exam level labels for images from the Siemens mammograph are available.
Unsupervised Domain Adaptation using Graph Transduction Games
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain.
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting
The heavy tail and skewness characteristics of such distributions in the spectral domain allow to capture long-range covariance of the signal in the time domain.
