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Found 18 papers, 6 papers with code
FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline
In this work we propose a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer's anatomical segmentation including surface reconstruction and cortical parcellation.
FatSegNet : A Fully Automated Deep Learning Pipeline for Adipose Tissue Segmentation on Abdominal Dixon MRI
Purpose: Development of a fast and fully automated deep learning pipeline (FatSegNet) to accurately identify, segment, and quantify abdominal adipose tissue on Dixon MRI from the Rhineland Study - a large prospective population-based study.
Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control
Next to voxel-wise uncertainty, we introduce four metrics to quantify structure-wise uncertainty in segmentation for quality control.
Learning Optimal Deep Projection of $^{18}$F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes
In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes.
InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation
We present a novel, parameter-efficient and practical fully convolutional neural network architecture, termed InfiNet, aimed at voxel-wise semantic segmentation of infant brain MRI images at iso-intense stage, which can be easily extended for other segmentation tasks involving multi-modalities.
Human Motion Analysis with Deep Metric Learning
Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail to appropriately capture the semantic relationship across motions and, as such, are not suitable for being employed as metrics within these tasks.
Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks
Increased information sharing through short and long-range skip connections between layers in fully convolutional networks have demonstrated significant improvement in performance for semantic segmentation.
Complex Fully Convolutional Neural Networks for MR Image Reconstruction
Undersampling the k-space data is widely adopted for acceleration of Magnetic Resonance Imaging (MRI).
SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis
Cross modal image syntheses is gaining significant interests for its ability to estimate target images of a different modality from a given set of source images, like estimating MR to MR, MR to CT, CT to PET etc, without the need for an actual acquisition. Though they show potential for applications in radiation therapy planning, image super resolution, atlas construction, image segmentation etc. The synthesis results are not as accurate as the actual acquisition. In this paper, we address the problem of multi modal image synthesis by proposing a fully convolutional deep learning architecture called the SynNet. We extend the proposed architecture for various input output configurations.
Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling
We introduce inherent measures for effective quality control of brain segmentation based on a Bayesian fully convolutional neural network, using model uncertainty.
Webly Supervised Learning for Skin Lesion Classification
Within medical imaging, manual curation of sufficient well-labeled samples is cost, time and scale-prohibitive.
Generalizability vs. Robustness: Adversarial Examples for Medical Imaging
In this paper, for the first time, we propose an evaluation method for deep learning models that assesses the performance of a model not only in an unseen test scenario, but also in extreme cases of noise, outliers and ambiguous input data.
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a \revision{MRI brain scan} in 20 seconds.
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
While large datasets of unlabeled image data are available in medical applications, access to manually labeled data is very limited.
ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network
Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers.
Learning Robust Hash Codes for Multiple Instance Image Retrieval
In this paper, for the first time, we introduce a multiple instance (MI) deep hashing technique for learning discriminative hash codes with weak bag-level supervision suited for large-scale retrieval.
Cross-Modal Manifold Learning for Cross-modal Retrieval
This paper presents a new scalable algorithm for cross-modal similarity preserving retrieval in a learnt manifold space.
Deep Residual Hashing
Hashing aims at generating highly compact similarity preserving code words which are well suited for large-scale image retrieval tasks.
