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Found 41 papers, 3 papers with code
Disguise without Disruption: Utility-Preserving Face De-Identification
While a variety of solutions have been proposed to de-identify such images, they often corrupt other non-identifying facial attributes that would be relevant for downstream tasks.
Exploring Cycle Consistency Learning in Interactive Volume Segmentation
To this end, we introduce a backward segmentation path that propagates the intermediate segmentation back to the starting slice using the same propagation network.
An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal
Previous studies tackled these two problems separately, where super resolution methods tend to enhance Gibbs artifacts, whereas Gibbs ringing removal methods tend to blur the images.
Computationally Efficient 3D MRI Reconstruction with Adaptive MLP
Recon3DMLP improves HR 3D reconstruction and outperforms several existing CNN-based models under similar GPU memory consumption, which demonstrates that Recon3DMLP is a practical solution for HR 3D MRI reconstruction.
Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI Reconstruction
Deep learning (DL)-based methods have shown promising results for single-slice MR reconstruction, but the addition of SMS acceleration raises unique challenges due to the composite k-space signals and the resulting images with strong inter-slice artifacts.
Progressive Multi-view Human Mesh Recovery with Self-Supervision
To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e. g., motion capture, sport analysis) and robustness to single-view ambiguities.
JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI
The proposed framework consists of a sampling mask generator for each image contrast and a reconstructor exploiting the inter-contrast correlations with a recurrent structure which enables the information sharing in a holistic way.
Forecasting Human Trajectory from Scene History
We categorize scene history information into two types: historical group trajectory and individual-surroundings interaction.
Federated Learning with Privacy-Preserving Ensemble Attention Distillation
We propose a privacy-preserving FL framework leveraging unlabeled public data for one-way offline knowledge distillation in this work.
PREF: Predictability Regularized Neural Motion Fields
We propose to regularize the estimated motion to be predictable.
Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation
Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively train a central model while the training data remains decentralized.
Self-supervised Human Mesh Recovery with Cross-Representation Alignment
However, on synthetic dense correspondence maps (i. e., IUV) few have been explored since the domain gap between synthetic training data and real testing data is hard to address for 2D dense representation.
Deep Statistic Shape Model for Myocardium Segmentation
Additionally, the predicted point cloud guarantees boundary correspondence for sequential images, which contributes to the downstream tasks, such as the motion estimation of myocardium.
PseudoClick: Interactive Image Segmentation with Click Imitation
The goal of click-based interactive image segmentation is to obtain precise object segmentation masks with limited user interaction, i. e., by a minimal number of user clicks.
Invertible Sharpening Network for MRI Reconstruction Enhancement
During inference, the learned blurring transform can be inverted to a sharpening transform leveraging the network's invertibility.
SMPL-A: Modeling Person-Specific Deformable Anatomy
In order to obtain accurate target location information, clinicians have to either conduct frequent intraoperative scans, resulting in higher exposition of patients to radiations, or adopt proxy procedures (e. g., creating and using custom molds to keep patients in the exact same pose during both preoperative organ scanning and subsequent treatment.
Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis
However, doing this accurately will require a large amount of disease localization annotations by clinical experts, a task that is prohibitively expensive to accomplish for most applications.
Learning Local Recurrent Models for Human Mesh Recovery
We conduct a variety of experiments on standard video mesh recovery benchmark datasets such as Human3. 6M, MPI-INF-3DHP, and 3DPW, demonstrating the efficacy of our design of modeling local dynamics as well as establishing state-of-the-art results based on standard evaluation metrics.
Ranked #4 on
3D Human Pose Estimation
on MPI-INF-3DHP
(PA-MPJPE metric)
Spatio-Temporal Representation Factorization for Video-based Person Re-Identification
To alleviate these problems, we propose Spatio-Temporal Representation Factorization (STRF), a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID.
Ranked #2 on
Person Re-Identification
on DukeMTMC-VideoReID
Everybody Is Unique: Towards Unbiased Human Mesh Recovery
Next, we present a simple baseline to address this problem that is scalable and can be easily used in conjunction with existing algorithms to improve their performance.
Ranked #1 on
3D Human Shape Estimation
on SSP-3D
(PVE-T metric)
Multi-scale Neural ODEs for 3D Medical Image Registration
The inference consists of iterative gradient updates similar to a conventional gradient descent optimizer but in a much faster way, because the neural ODE learns from the training data to adapt the gradient efficiently at each iteration.
Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning
We propose a deep learning framework for undersampled 3D MRI data reconstruction and apply it to MULTIPLEX MRI.
Cardiac Functional Analysis with Cine MRI via Deep Learning Reconstruction
To the best of our knowledge, this is the first work to evaluate the cine MRI with deep learning reconstruction for cardiac function analysis and compare it with other conventional methods.
A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts
Despite substantial progress in applying neural networks (NN) to a wide variety of areas, they still largely suffer from a lack of transparency and interpretability.
Ensemble Attention Distillation for Privacy-Preserving Federated Learning
Such decentralized training naturally leads to issues of imbalanced or differing data distributions among the local models and challenges in fusing them into a central model.
Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks
We also analyze thickness patterns on different cardiac pathologies with a standard clinical model and the results demonstrate the potential clinical value of our method for thickness based cardiac disease diagnosis.
Anatomy-Aware Cardiac Motion Estimation
A baseline dense motion tracker is trained to approximate the motion fields and then refined to estimate anatomy-aware motion fields under the weak supervision from the VAE.
Towards Visually Explaining Similarity Models
We show that the resulting similarity models perform, and can be visually explained, better than the corresponding baseline models trained without these constraints.
Real-Time Cardiac Cine MRI with Residual Convolutional Recurrent Neural Network
Real-time cardiac cine MRI does not require ECG gating in the data acquisition and is more useful for patients who can not hold their breaths or have abnormal heart rhythms.
Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation
In this paper, we propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation.
MRI Image Reconstruction via Learning Optimization Using Neural ODEs
We investigate several models based on three ODE solvers and compare models with fixed solvers and learned solvers.
FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation
In clinical deployment, however, they suffer dramatic performance drops due to mismatched distributions between training and testing datasets, commonly encountered in the clinical environment.
Hierarchical Kinematic Human Mesh Recovery
In this work, we address this gap by proposing a new technique for regression of human parametric model that is explicitly informed by the known hierarchical structure, including joint interdependencies of the model.
Pyramid Convolutional RNN for MRI Image Reconstruction
We evaluate our model on the fastMRI knee and brain datasets and the results show that the proposed model outperforms other methods and can recover more details.
Visual Similarity Attention
While there has been substantial progress in learning suitable distance metrics, these techniques in general lack transparency and decision reasoning, i. e., explaining why the input set of images is similar or dissimilar.
Structure-Aware Shape Synthesis
We propose a new procedure to guide training of a data-driven shape generative model using a structure-aware loss function.
Generating Synthetic X-ray Images of a Person from the Surface Geometry
Furthermore, the synthetic X-ray image is parametrized and can be manipulated by adjusting a set of body markers which are also generated during the X-ray image prediction.
DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD Models for 2.5D Recognition
Recent progress in computer vision has been dominated by deep neural networks trained over large amounts of labeled data.
Deep Decision Network for Multi-Class Image Classification
During the learning phase, starting from the root network node, DDN automatically builds a network that splits the data into disjoint clusters of classes which would be handled by the subsequent expert networks.
BodyPrint: Pose Invariant 3D Shape Matching of Human Bodies
3D human body shape matching has large potential on many real world applications, especially with the recent advances in the 3D range sensing technology.
