Search Results for author:
Found 76 papers, 23 papers with code
KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion
Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candidate.
AoSRNet: All-in-One Scene Recovery Networks via Multi-knowledge Integration
Additionally, we suggest a multi-receptive field extraction module (MEM) to attenuate the loss of image texture details caused by GC nonlinear and OLS linear transformations.
Frame-Wise Breath Detection with Self-Training: An Exploration of Enhancing Breath Naturalness in Text-to-Speech
Developing Text-to-Speech (TTS) systems that can synthesize natural breath is essential for human-like voice agents but requires extensive manual annotation of breath positions in training data.
MvKSR: Multi-view Knowledge-guided Scene Recovery for Hazy and Rainy Degradation
High-quality imaging is crucial for ensuring safety supervision and intelligent deployment in fields like transportation and industry.
LUPET: Incorporating Hierarchical Information Path into Multilingual ASR
Many factors have separately shown their effectiveness on improving multilingual ASR.
Expanding the Vocabulary of BERT for Knowledge Base Construction
To address this, we present Vocabulary Expandable BERT for knowledge base construction, which expand the language model's vocabulary while preserving semantic embeddings for newly added words.
Ranked #1 on
Knowledge Base Population
on LM-KBC 2023
Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge
Kidney and Kidney Tumor Segmentation Challenge (KiTS) 2023 offers a platform for researchers to compare their solutions to segmentation from 3D CT.
Aorta Segmentation from 3D CT in MICCAI SEG.A. 2023 Challenge
In this work, we describe our solution to the Segmentation of the Aorta (SEG. A. 231) from 3D CT challenge.
FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models
However, applying FL to finetune PLMs is hampered by challenges, including restricted model parameter access, high computational requirements, and communication overheads.
Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training
We curate a large-scale dataset to enable pre-training of 3D medical radiology images (MRI and CT).
Neural Deformable Models for 3D Bi-Ventricular Heart Shape Reconstruction and Modeling from 2D Sparse Cardiac Magnetic Resonance Imaging
We model the bi-ventricular shape using blended deformable superquadrics, which are parameterized by a set of geometric parameter functions and are capable of deforming globally and locally.
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models
To address this issue in prompt engineering, we propose a new and effective approach called Prompt Space.
Edit Everything: A Text-Guided Generative System for Images Editing
We introduce a new generative system called Edit Everything, which can take image and text inputs and produce image outputs.
Fair Federated Medical Image Segmentation via Client Contribution Estimation
Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness).
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples
We propose a practical vertical federated learning (VFL) framework called \textbf{one-shot VFL} that can solve the communication bottleneck and the problem of limited overlapping samples simultaneously based on semi-supervised learning.
Data-Driven Leader-following Consensus for Nonlinear Multi-Agent Systems against Composite Attacks: A Twins Layer Approach
Consequently, the resilient control task against CAs can be divided into two parts: One is distributed estimation against DoS attacks on the TL and the other is resilient decentralized tracking control against actuation attacks on the CPL.
Duration-aware pause insertion using pre-trained language model for multi-speaker text-to-speech
We also leverage duration-aware pause insertion for more natural multi-speaker TTS.
Fast and Blind Speech Copy-Move Detection and Localization in Noise
Copy-move forgery on speech (CMF), coupled with post-processing techniques, presents a great challenge to the forensic detection and localization of tampered areas.
MONAI: An open-source framework for deep learning in healthcare
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs
However, it remains challenging to model the negation and union operator.
NVIDIA FLARE: Federated Learning from Simulation to Real-World
Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data.
Automated head and neck tumor segmentation from 3D PET/CT
Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platform for researchers to compare their solutions to segmentation of tumors and lymph nodes from 3D CT and PET images.
Automated segmentation of intracranial hemorrhages from 3D CT
Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a platform for researchers to compare their solutions to segmentation of hemorrhage stroke regions from 3D CTs.
Automated ischemic stroke lesion segmentation from 3D MRI
Ischemic Stroke Lesion Segmentation challenge (ISLES 2022) offers a platform for researchers to compare their solutions to 3D segmentation of ischemic stroke regions from 3D MRIs.
Warm Start Active Learning with Proxy Labels \& Selection via Semi-Supervised Fine-Tuning
Which volume to annotate next is a challenging problem in building medical imaging datasets for deep learning.
Low-Complexity Acoustic Echo Cancellation with Neural Kalman Filtering
The Kalman filter has been adopted in acoustic echo cancellation due to its robustness to double-talk, fast convergence, and good steady-state performance.
Multi-stage Moving Target Defense: A Security-enhanced D-FACTS Implementation Approach
We prove that a designed MMTD can significantly improve the detection capability compared to existing one-stage MTDs.
UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation
Vision Transformers (ViT)s have recently become popular due to their outstanding modeling capabilities, in particular for capturing long-range information, and scalability to dataset and model sizes which has led to state-of-the-art performance in various computer vision and medical image analysis tasks.
Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation
In this work, we propose a novel training framework FedSM to avoid the client drift issue and successfully close the generalization gap compared with the centralized training for medical image segmentation tasks for the first time.
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing.
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity.
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet
Given the recent success of deep learning in medical image segmentation, Neural Architecture Search (NAS) has been introduced to find high-performance 3D segmentation network architectures.
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Vision Transformers (ViT)s have shown great performance in self-supervised learning of global and local representations that can be transferred to downstream applications.
Ranked #1 on
Medical Image Segmentation
on Synapse multi-organ CT
(using extra training data)
T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging
Lesion segmentation in medical imaging has been an important topic in clinical research.
Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging
Multiple instance learning (MIL) is a key algorithm for classification of whole slide images (WSI).
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data.
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures
Building robust deep learning-based models requires diverse training data, ideally from several sources.
The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization in Medical Image Segmentation
First, we show higher correlation to using full data for training when testing on the external validation set using smaller proxy data than a random selection of the proxy data.
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation
In this work, we design a new data-driven approach, namely Auto-FedAvg, where aggregation weights are dynamically adjusted, depending on data distributions across data silos and the current training progress of the models.
Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays
In this work, we introduce an image-text pre-training framework that can learn from these raw data with mixed data inputs, i. e., paired image-text data, a mixture of paired and unpaired data.
DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
In this work, we focus on three important aspects of NAS in 3D medical image segmentation: flexible multi-path network topology, high search efficiency, and budgeted GPU memory usage.
UNETR: Transformers for 3D Medical Image Segmentation
Inspired by the recent success of transformers for Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a sequence-to-sequence prediction problem.
DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images
Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard for regional myocardium deformation and cardiac strain estimation.
I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling
Reconstructing a high-precision and high-fidelity 3D human hand from a color image plays a central role in replicating a realistic virtual hand in human-computer interaction and virtual reality applications.
Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation
The primary advantage being that active learning frameworks select data points that can accelerate the learning process of a model and can reduce the amount of data needed to achieve full accuracy as compared to a model trained on a randomly acquired data set.
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results.
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
The performance of deep learning-based methods strongly relies on the number of datasets used for training.
Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning
The training of deep learning models typically requires extensive data, which are not readily available as large well-curated medical-image datasets for development of artificial intelligence (AI) models applied in Radiology.
Going to Extremes: Weakly Supervised Medical Image Segmentation
Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation.
Learning Image Labels On-the-fly for Training Robust Classification Models
We apply the attention-on-label scheme on the classification task of a synthetic noisy CIFAR-10 dataset to prove the concept, and then demonstrate superior results (3-5% increase on average in multiple disease classification AUCs) on the chest x-ray images from a hospital-scale dataset (MIMIC-CXR) and hand-labeled dataset (OpenI) in comparison to regular training paradigms.
Enhanced MRI Reconstruction Network using Neural Architecture Search
The accurate reconstruction of under-sampled magnetic resonance imaging (MRI) data using modern deep learning technology, requires significant effort to design the necessary complex neural network architectures.
PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data
The 3D volumetric shape of the heart's left ventricle (LV) myocardium (MYO) wall provides important information for diagnosis of cardiac disease and invasive procedure navigation.
Time-aware Graph Embedding: A temporal smoothness and task-oriented approach
Knowledge graph embedding, which aims to learn the low-dimensional representations of entities and relationships, has attracted considerable research efforts recently.
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Experiments on the NIH pancreas segmentation dataset and a multi-organ segmentation dataset show state-of-the-art performance of the proposed framework on semi-supervised medical image segmentation.
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation
For example, fully convolutional neural networks (FCN) achieve the state-of-the-art performance in several applications of 2D/3D medical image segmentation.
Enhancing Foreground Boundaries for Medical Image Segmentation
Object segmentation plays an important role in the modern medical image analysis, which benefits clinical study, disease diagnosis, and surgery planning.
Unifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition
Overall, our work unified graph embedding features to promotes systematic research on human action recognition.
Training Models 20X Faster in Medical Image Analysis
Analyzing high-dimensional medical images (2D/3D/4D CT, MRI, histopathological images, etc.)
Correlation via Synthesis: End-to-end Image Generation and Radiogenomic Learning Based on Generative Adversarial Network
In this work, we investigate the potential of an end-to-end method fusing gene code with image features to generate synthetic pathology image and learn radiogenomic map simultaneously.
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
3D convolution neural networks (CNN) have been proved very successful in parsing organs or tumours in 3D medical images, but it remains sophisticated and time-consuming to choose or design proper 3D networks given different task contexts.
End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation
However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs.
Cardiac Segmentation of LGE MRI with Noisy Labels
In the first step, we register a small set of five LGE cardiac magnetic resonance (CMR) images with ground truth labels to a set of 40 target LGE CMR images without annotation.
Weakly supervised segmentation from extreme points
Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images.
Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network
Radiogenomic map linking image features and gene expression profiles is useful for noninvasively identifying molecular properties of a particular type of disease.
4D CNN for semantic segmentation of cardiac volumetric sequences
We propose a 4D convolutional neural network (CNN) for the segmentation of retrospective ECG-gated cardiac CT, a series of single-channel volumetric data over time.
When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation
We rethink data augmentation for medical 3D images and propose a deep stacked transformations (DST) approach for domain generalization.
V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation
Deep learning algorithms, in particular 2D and 3D fully convolutional neural networks (FCNs), have rapidly become the mainstream methodology for volumetric medical image segmentation.
An Alarm System For Segmentation Algorithm Based On Shape Model
Motivated by this, in this paper, we learn a feature space using the shape information which is a strong prior shared among different datasets and robust to the appearance variation of input data. The shape feature is captured using a Variational Auto-Encoder (VAE) network that trained with only the ground truth masks.
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training
Meanwhile, a fully-supervised method based on our approach achieved state-of-the-art performances on both the LiTS liver tumor segmentation and the Medical Segmentation Decathlon (MSD) challenge, demonstrating the robustness and value of our framework, even when fully supervised training is feasible.
MRI Reconstruction via Cascaded Channel-wise Attention Network
We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate.
Important Attribute Identification in Knowledge Graph
The knowledge graph(KG) composed of entities with their descriptions and attributes, and relationship between entities, is finding more and more application scenarios in various natural language processing tasks.
Saliency deep embedding for aurora image search
In this paper, we aim to explore a new search method for images captured with circular fisheye lens, especially the aurora images.
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment.
Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
In this paper, we propose an automatic and fast algorithm to localize and label the vertebra centroids in 3D CT volumes.
Collaborative Discriminant Locality Preserving Projections With its Application to Face Recognition
In our algorithm, the discriminating power of DLPP are further exploited from two aspects.
