Search Results for author:
Found 31 papers, 12 papers with code
From Registration Uncertainty to Segmentation Uncertainty
Here, we propose a novel framework to concurrently estimate both the epistemic and aleatoric segmentation uncertainties for image registration.
Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning?
This is a factor that has been overlooked in prior research on tMRI post-processing.
GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation, Demonstration, and Imitation
We further introduce a distillation-friendly demonstration generation method that automatically generates a million high-quality demonstrations suitable for learning.
Semidefinite Relaxations of the Gromov-Wasserstein Distance
The Gromov-Wasserstein (GW) distance is a variant of the optimal transport problem that allows one to match objects between incomparable spaces.
Semantic Complete Scene Forecasting from a 4D Dynamic Point Cloud Sequence
To tackle this challenging problem, we properly model the synergetic relationship between future forecasting and semantic scene completion through a novel network named SCSFNet.
Learning node representation via Motif Coarsening
For the motif-based node representation learning process, we propose a Motif Coarsening strategy for incorporating motif structure into the graph representation learning process.
Music Source Separation Based on a Lightweight Deep Learning Framework (DTTNET: DUAL-PATH TFC-TDF UNET)
Music source separation (MSS) aims to extract 'vocals', 'drums', 'bass' and 'other' tracks from a piece of mixed music.
Ranked #8 on
Music Source Separation
on MUSDB18-HQ
MomentaMorph: Unsupervised Spatial-Temporal Registration with Momenta, Shooting, and Correction
We introduce a novel "momenta, shooting, and correction" framework for Lagrangian motion estimation in the presence of repetitive patterns and large motion.
A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond
Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade.
Learning to Evaluate the Artness of AI-generated Images
Assessing the artness of AI-generated images continues to be a challenge within the realm of image generation.
Predicting Adverse Neonatal Outcomes for Preterm Neonates with Multi-Task Learning
In this work, we first analyze the correlations between three adverse neonatal outcomes and then formulate the diagnosis of multiple neonatal outcomes as a multi-task learning (MTL) problem.
An investigation of licensing of datasets for machine learning based on the GQM model
And in the development of machine learning systems, the most widely used are publicly available datasets.
Deformable Cross-Attention Transformer for Medical Image Registration
Transformers have recently shown promise for medical image applications, leading to an increasing interest in developing such models for medical image registration.
Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration
In the past, optimization-based registration models have used spatially-varying regularization to account for deformation variations in different image regions.
SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images
Inspired by the recent success of Vision Transformers and advances in multi-modal image analysis, we propose a novel segmentation model, debuted, Cross-Modal Swin Transformer (SwinCross), with cross-modal attention (CMA) module to incorporate cross-modal feature extraction at multiple resolutions. To validate the effectiveness of the proposed method, we performed experiments on the HECKTOR 2021 challenge dataset and compared it with the nnU-Net (the backbone of the top-5 methods in HECKTOR 2021) and other state-of-the-art transformer-based methods such as UNETR, and Swin UNETR.
LeaF: Learning Frames for 4D Point Cloud Sequence Understanding
With such frames, we can factorize geometry and motion to facilitate a feature-space geometric reconstruction for more effective 4D learning.
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation
Inspired by the recent success of Transformers for Natural Language Processing and vision Transformer for Computer Vision, many researchers in the medical imaging community have flocked to Transformer-based networks for various main stream medical tasks such as classification, segmentation, and estimation.
On Finite Difference Jacobian Computation in Deformable Image Registration
For digital transformations, |J| is commonly approximated using a central difference, but this strategy can yield positive |J|'s for transformations that are clearly not diffeomorphic -- even at the voxel resolution level.
Improving Visual-textual Sentiment Analysis by Fusing Expert Features
Visual-textual sentiment analysis aims to predict sentiment with the input of a pair of image and text.
A Unified Framework against Topology and Class Imbalance
We develop a multi-class AUC optimization work to deal with the class imbalance problem.
Recurrent Meta-Learning against Generalized Cold-start Problem in CTR Prediction
To attack this problem, we propose a recursive meta-learning model with the user's behavior sequence prediction as a separate training task.
Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising
In this work, we proposed an efficient spatial and channel-wise encoder-decoder transformer, Spach Transformer, that can leverage spatial and channel information based on local and global MSAs.
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, the latest technological advance of deep learning, has gained prevalence in natural language processing or computer vision.
A Noise-level-aware Framework for PET Image Denoising
Our hypothesis is that by explicitly providing the local relative noise level of the input image to a deep convolutional neural network (DCNN), the DCNN can outperform itself trained on image appearance only.
TransMorph: Transformer for unsupervised medical image registration
Recently Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications.
Ranked #1 on
Medical Image Registration
on OASIS
Targeted Gradient Descent: A Novel Method for Convolutional Neural Networks Fine-tuning and Online-learning
In this study, we present Targeted Gradient Descent (TGD), a novel fine-tuning method that can extend a pre-trained network to a new task without revisiting data from the previous task while preserving the knowledge acquired from previous training.
Learning Fuzzy Clustering for SPECT/CT Segmentation via Convolutional Neural Networks
The results demonstrated that the proposed method provides fast and robust lesion and bone segmentation for QBSPECT/CT.
ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration
However, the performances of ConvNets are still limited by lacking the understanding of long-range spatial relations in an image.
Ranked #4 on
Medical Image Registration
on OASIS
Medical Image Segmentation via Unsupervised Convolutional Neural Network
For the majority of the learning-based segmentation methods, a large quantity of high-quality training data is required.
Generating Anthropomorphic Phantoms Using Fully Unsupervised Deformable Image Registration with Convolutional Neural Networks
In this work, we present a novel image registration method for creating highly anatomically detailed anthropomorphic phantoms from a single digital phantom.
Feature-Based Image Clustering and Segmentation Using Wavelets
Pixel intensity is a widely used feature for clustering and segmentation algorithms, the resulting segmentation using only intensity values might suffer from noises and lack of spatial context information.
