Search Results for author:
Found 23 papers, 10 papers with code
An Event-centric Framework for Predicting Crime Hotspots with Flexible Time Intervals
Furthermore, we introduce a type-aware spatiotemporal point process that learns crime-evolving features, measuring the risk of specific crime types at a given time and location by considering the frequency of past crime events.
NODER: Image Sequence Regression Based on Neural Ordinary Differential Equations
Regression on medical image sequences can capture temporal image pattern changes and predict images at missing or future time points.
Med3DInsight: Enhancing 3D Medical Image Understanding with 2D Multi-Modal Large Language Models
Understanding 3D medical image volumes is a critical task in the medical domain.
AliFuse: Aligning and Fusing Multi-modal Medical Data for Computer-Aided Diagnosis
Medical data collected for diagnostic decisions are typically multimodal, providing comprehensive information on a subject.
Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors
In this paper, we propose a scribble-based volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image segmentation and aims to its improve boundary prediction.
MedBLIP: Bootstrapping Language-Image Pre-training from 3D Medical Images and Texts
Vision-language pre-training (VLP) models have been demonstrated to be effective in many computer vision applications.
MetaRegNet: Metamorphic Image Registration Using Flow-Driven Residual Networks
We desire an efficient solution to jointly account for spatial deformations and appearance changes in the pathological regions where the correspondences are missing, i. e., finding a solution to metamorphic image registration.
Longformer: Longitudinal Transformer for Alzheimer's Disease Classification with Structural MRIs
Structural magnetic resonance imaging (sMRI) is widely used for brain neurological disease diagnosis; while longitudinal MRIs are often collected to monitor and capture disease progression, as clinically used in diagnosing Alzheimer's disease (AD).
Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations
In this paper, we propose a scribble-based volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image segmentation and improves boundary prediction.
Federated Learning Cost Disparity for IoT Devices
We model the learning gain achieved by an IoT device against its participation cost as its utility.
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network
In this paper we consider the problem of unsupervised anomaly segmentation in medical images, which has attracted increasing attention in recent years due to the expensive pixel-level annotations from experts and the existence of a large amount of unannotated normal and abnormal image scans.
Utility Fairness for the Differentially Private Federated Learning
We identify that this results in utility unfairness because the same global model is shared among the devices.
DDR-Net: Dividing and Downsampling Mixed Network for Diffeomorphic Image Registration
Deep diffeomorphic registration faces significant challenges for high-dimensional images, especially in terms of memory limits.
MDA-Net: Multi-Dimensional Attention-Based Neural Network for 3D Image Segmentation
Segmenting an entire 3D image often has high computational complexity and requires large memory consumption; by contrast, performing volumetric segmentation in a slice-by-slice manner is efficient but does not fully leverage the 3D data.
Orthogonal Time Sequency Multiplexing Modulation: Analysis and Low-Complexity Receiver Design
This paper proposes orthogonal time sequency multiplexing (OTSM), a novel single carrier modulation scheme that places information symbols in the delay-sequency domain followed by a cascade of time-division multiplexing (TDM) and Walsh-Hadamard sequence multiplexing.
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation
We introduce a neural network framework, utilizing adversarial learning to partition an image into two cuts, with one cut falling into a reference distribution provided by the user.
Hybrid Cascaded Neural Network for Liver Lesion Segmentation
Automatic liver lesion segmentation is a challenging task while having a significant impact on assisting medical professionals in the designing of effective treatment and planning proper care.
On the Performance of Low-Altitude UAV-Enabled Secure AF Relaying with Cooperative Jamming and SWIPT
This paper proposes a novel cooperative secure unmanned aerial vehicle (UAV) aided transmission protocol, where a source (Alice) sends confidential information to a destination (Bob) via an energy-constrained UAV-mounted amplify-and-forward (AF) relay in the presence of a ground eavesdropper (Eve).
Information Theory Signal Processing Information Theory
Predictive Image Regression for Longitudinal Studies with Missing Data
Features extracted by the LSTM and CNN are fed into a decoder network to reconstruct the vector momentum sequence, which is used for the image sequence prediction by deforming the baseline image with LDDMM shooting.
CompNet: Complementary Segmentation Network for Brain MRI Extraction
Brain extraction is a fundamental step for most brain imaging studies.
Diagnostic Classification Of Lung Nodules Using 3D Neural Networks
Lung cancer is the leading cause of cancer-related death worldwide.
Parametric Regression on the Grassmannian
We then specialize this idea to the Grassmann manifold and demonstrate that it yields a simple, extensible and easy-to-implement solution to the parametric regression problem.
Unsupervised Learning of Dictionaries of Hierarchical Compositional Models
Given a set of unannotated training images, a dictionary of such hierarchical templates are learned so that each training image can be represented by a small number of templates that are spatially translated, rotated and scaled versions of the templates in the learned dictionary.
