Search Results for author:
Found 28 papers, 7 papers with code
Multimodal Learning To Improve Cardiac Late Mechanical Activation Detection From Cine MR Images
This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images.
An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping
These results underscore the efficacy of the ICA-net and MCIF-net deep learning pipeline in learning the ICA structure's distribution and automating MCIF computation with PV corrections.
MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image Deformations
In this paper, we propose a novel model, Multimodal Geometric Augmentation (MGAug), that for the first time generates augmenting transformations in a multimodal latent space of geometric deformations.
Implement services for business scenarios by combining basic emulators
This article mainly introduces how to use various basic emulators to form a combined emulator in the Jiutian Intelligence Network Simulation Platform to realize simulation service functions in different business scenarios.
Emulators in JINSP
JINSP(Jiutian Intelligence Network Simulation Platform) describes a series of basic emulators and their combinations, such as the simulation of the protocol stack for dynamic users in a real environment, which is composed of user behavior simulation, base station simulation, and terminal simulation.
Design of JiuTian Intelligent Network Simulation Platform
This paper introduced the JiuTian Intelligent Network Simulation Platform, which can provide wireless communication simulation data services for the Open Innovation Platform.
SADIR: Shape-Aware Diffusion Models for 3D Image Reconstruction
In this paper, we propose a shape-aware network based on diffusion models for 3D image reconstruction, named SADIR, to address these issues.
NeurEPDiff: Neural Operators to Predict Geodesics in Deformation Spaces
To achieve this, we develop a neural operator that for the first time learns the evolving trajectory of geodesic deformations parameterized in the tangent space of diffeomorphisms(a. k. a velocity fields).
MetaMorph: Learning Metamorphic Image Transformation With Appearance Changes
This paper presents a novel predictive model, MetaMorph, for metamorphic registration of images with appearance changes (i. e., caused by brain tumors).
Multimodal Deep Learning to Differentiate Tumor Recurrence from Treatment Effect in Human Glioblastoma
3D parametric PET Ki (from dPET), traditional static PET standardized uptake values (SUV), and also the brain tumor MR voxels formed the input for the CNN.
Joint Deep Learning for Improved Myocardial Scar Detection from Cardiac MRI
Automated identification of myocardial scar from late gadolinium enhancement cardiac magnetic resonance images (LGE-CMR) is limited by image noise and artifacts such as those related to motion and partial volume effect.
Multitask Learning for Improved Late Mechanical Activation Detection of Heart from Cine DENSE MRI
With a newly introduced auxiliary LMA region classification sub-network, our proposed model shows more robustness to the complex pattern cause by myocardial scar, significantly eliminates their negative effects in LMA detection, and in turn improves the performance of scar classification.
Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers
We introduce a newly designed framework that (i) simultaneously derives features from both image and latent shape spaces with large intra-class variations; and (ii) gains increased model interpretability by allowing direct access to the underlying geometric features of image data.
BDG-Net: Boundary Distribution Guided Network for Accurate Polyp Segmentation
However, due to the different sizes of polyps and the unclear boundary between polyps and their surrounding mucosa, it is challenging to segment polyps accurately.
Ranked #20 on
Medical Image Segmentation
on Kvasir-SEG
Hybrid Atlas Building with Deep Registration Priors
Registration-based atlas building often poses computational challenges in high-dimensional image spaces.
Bayesian Atlas Building with Hierarchical Priors for Subject-specific Regularization
This paper presents a novel hierarchical Bayesian model for unbiased atlas building with subject-specific regularizations of image registration.
Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods
Machine learning (ML) models used in medical imaging diagnostics can be vulnerable to a variety of privacy attacks, including membership inference attacks, that lead to violations of regulations governing the use of medical data and threaten to compromise their effective deployment in the clinic.
Deep Learning for Regularization Prediction in Diffeomorphic Image Registration
This paper presents a predictive model for estimating regularization parameters of diffeomorphic image registration.
PAC Model Checking of Black-Box Continuous-Time Dynamical Systems
In this paper we present a novel model checking approach to finite-time safety verification of black-box continuous-time dynamical systems within the framework of probably approximately correct (PAC) learning.
Systems and Control Formal Languages and Automata Theory Systems and Control
DeepFLASH: An Efficient Network for Learning-based Medical Image Registration
This paper presents DeepFLASH, a novel network with efficient training and inference for learning-based medical image registration.
Learning One-Clock Timed Automata
We present an algorithm for active learning of deterministic timed automata with a single clock.
Formal Languages and Automata Theory
Mixture Probabilistic Principal Geodesic Analysis
Dimensionality reduction on Riemannian manifolds is challenging due to the complex nonlinear data structures.
Temporal Registration in Application to In-utero MRI Time Series
To achieve accurate and robust alignment, we make a Markov assumption on the nature of motion and take advantage of the temporal smoothness in the image data.
A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
Kernels of the GP are estimated by using variograms and a discrete grid search method.
On the Applicability of Registration Uncertainty
For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters.
SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation
We then propose a Bayesian model that combines the supervised and the unsupervised information for probabilistic learning.
Electron Microscopy Image Segmentation
Image Segmentation
+2
Temporal Registration in In-Utero Volumetric MRI Time Series
We present a robust method to correct for motion and deformations for in-utero volumetric MRI time series.
Probabilistic Principal Geodesic Analysis
Principal geodesic analysis (PGA) is a generalization of principal component analysis (PCA) for dimensionality reduction of data on a Riemannian manifold.
