Search Results for author:
Found 34 papers, 9 papers with code
JSover: Joint Spectrum Estimation and Multi-Material Decomposition from Single-Energy CT Projections
Multi-material decomposition (MMD) enables quantitative reconstruction of tissue compositions in the human body, supporting a wide range of clinical applications.
Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction
Emerging unsupervised implicit neural representation (INR) methods, such as NeRP, NeAT, and SCOPE, have shown great potential to address sparse-view computed tomography (SVCT) inverse problems.
The 2nd Place Solution from the 3D Semantic Segmentation Track in the 2024 Waymo Open Dataset Challenge
3D semantic segmentation is one of the most crucial tasks in driving perception.
Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT
Based on a theoretical analysis of ring artifact formation, we reformulate RAR as a multi-parameter inverse problem, where the non-ideal responses of X-ray detectors are parameterized as solvable physical variables.
ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism
Intracerebral hemorrhage (ICH) is the most fatal subtype of stroke and is characterized by a high incidence of disability.
Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation
Motion correction (MoCo) in radial MRI is a challenging problem due to the unpredictability of subject's motion.
LPUWF-LDM: Enhanced Latent Diffusion Model for Precise Late-phase UWF-FA Generation on Limited Dataset
Our Latent Diffusion Model for Ultra-Wide-Field Late-Phase Fluorescein Angiography (LPUWF-LDM) effectively reconstructs fine details in late-phase UWF-FA and achieves state-of-the-art results compared to other existing methods when working with limited datasets.
Class-balanced Open-set Semi-supervised Object Detection for Medical Images
In this paper, we consider the open-set semi-supervised object detection problem which leverages unlabeled data that contain OOD classes to improve object detection for medical images.
Scalable and Adaptive Spectral Embedding for Attributed Graph Clustering
In this paper, we introduce Scalable and Adaptive Spectral Embedding (SASE), a simple attributed graph clustering method devoid of parameter learning.
OvSW: Overcoming Silent Weights for Accurate Binary Neural Networks
We observe that, for vanilla BNNs, over 50\% of the weights remain their signs unchanged during training, and these weights are not only distributed at the tails of the weight distribution but also universally present in the vicinity of zero.
Sparse-view Signal-domain Photoacoustic Tomography Reconstruction Method Based on Neural Representation
Photoacoustic tomography is a hybrid biomedical technology, which combines the advantages of acoustic and optical imaging.
Solving Energy-Independent Density for CT Metal Artifact Reduction via Neural Representation
In this work, we propose Density neural representation (Diner), a novel unsupervised MAR method.
DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction
Additionally, it effectively augments the feasibility of the solution space for the inverse problem through the generative diffusion model, resulting in increased stability and precision in the solutions.
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI
Benefiting from the powerful continuous representation and joint estimation of the MRI image and coil sensitivities, IMJENSE outperforms conventional image or k-space domain reconstruction algorithms.
Magnetic Field-Based Reward Shaping for Goal-Conditioned Reinforcement Learning
Existing reward shaping methods for goal-conditioned RL are typically built on distance metrics with a linear and isotropic distribution, which may fail to provide sufficient information about the ever-changing environment with high complexity.
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
In this work, we present a novel Polychromatic neural representation (Polyner) to tackle the challenging problem of CT imaging when metallic implants exist within the human body.
Self-supervised arbitrary scale super-resolution framework for anisotropic MRI
The anisotropic volume's high-resolution (HR) plane is used to build the HR-LR image pairs for model training.
Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction
The high-quality and inner continuity of the images provided by INR has great potential to further improve the spatiotemporal resolution of dynamic MRI, without the need of any training data.
Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field
NeRF-based SVCT methods represent the desired CT image as a continuous function of spatial coordinates and train a Multi-Layer Perceptron (MLP) to learn the function by minimizing loss on the SV sinogram.
A scan-specific unsupervised method for parallel MRI reconstruction via implicit neural representation
This function was parameterized by a neural network and learned directly from the measured k-space itself without additional fully sampled high-quality training data.
Continuous longitudinal fetus brain atlas construction via implicit neural representation
Using implicit neural representation, we construct a continuous and noise-free longitudinal fetus brain atlas as a function of the 4D spatial-temporal coordinate.
Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image
Experimental results of simulated noise and real microscopy noise removal show that Noise2SR outperforms two blind-spot based self-supervised deep learning image denoising methods.
Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomography
Compared with recent related works that solve similar problems using implicit neural representation network (INR), our essential contribution is an effective and simple re-projection strategy that pushes the tomography image reconstruction quality over supervised deep learning CT reconstruction works.
An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation
In the ArSSR model, the reconstruction of HR images with different up-scaling rates is defined as learning a continuous implicit voxel function from the observed LR images.
IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation
For collecting high-quality high-resolution (HR) MR image, we propose a novel image reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR images and achieve an arbitrary up-sampling rate for HR image reconstruction.
Wheel-Rail Interface Condition Estimation (W-RICE)
The surface roughness between the wheel and rail has a huge influence on rolling noise level.
Cross-Channel Intragroup Sparsity Neural Network
Modern deep neural networks rely on overparameterization to achieve state-of-the-art generalization.
Adabot: Fault-Tolerant Java Decompiler
Specifically, in terms of BLEU-4 and Word Error Rate (WER), our performance has reached 94. 50% and 2. 65% on the redundant test set; 92. 30% and 3. 48% on the purified test set.
Improved Expressivity Through Dendritic Neural Networks
In this study, we model such local nonlinearity of dendritic trees with our dendritic neural network (DENN) structure and apply this structure to typical machine learning tasks.
Shape-from-Mask: A Deep Learning Based Human Body Shape Reconstruction from Binary Mask Images
In this paper, we propose a deep learning based reconstruction of 3D human body shape from 2D orthographic views.
Long short-term memory networks in memristor crossbars
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence.
Emerging Technologies Applied Physics
MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks
Our experiments show that different adversarial strengths, i. e., perturbation levels of adversarial examples, have different working zones to resist the attack.
Generative Poisoning Attack Method Against Neural Networks
A countermeasure is also designed to detect such poisoning attack methods by checking the loss of the target model.
A New Learning Method for Inference Accuracy, Core Occupation, and Performance Co-optimization on TrueNorth Chip
IBM TrueNorth chip uses digital spikes to perform neuromorphic computing and achieves ultrahigh execution parallelism and power efficiency.
