Search Results for author:
Found 15 papers, 6 papers with code
BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation
Specifically, BRAU-Net++ uses bi-level routing attention as the core building block to design our u-shaped encoder-decoder structure, in which both encoder and decoder are hierarchically constructed, so as to learn global semantic information while reducing computational complexity.
EAFP-Med: An Efficient Adaptive Feature Processing Module Based on Prompts for Medical Image Detection
EAFP-Med can efficiently extract lesion features from various medical images based on prompts, enhancing the model's performance.
Review of AlexNet for Medical Image Classification
In recent years, the rapid development of deep learning has led to a wide range of applications in the field of medical image classification.
EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification
In this paper, we propose a novel multi-source domain generalization framework called EEG-DG, which leverages multiple source domains with different statistical distributions to build generalizable models on unseen target EEG data.
TBDLNet: a network for classifying multidrug-resistant and drug-sensitive tuberculosis
Five indexes are selected in this paper, which are accuracy, sensitivity, precision, F1-score, and specificity.
DNN-DANM: A High-Accuracy Two-Dimensional DOA Estimation Method Using Practical RIS
After formulating the system model with the mutual coupling effect and the reflection phase/amplitude errors of the RIS, a novel DNNDANM method is proposed for the DOA estimation by combining the deep neural network (DNN) and the decoupling atomic norm minimization (DANM).
Multi-resolution Spatiotemporal Enhanced Transformer Denoising with Functional Diffusive GANs for Constructing Brain Effective Connectivity in MCI analysis
In this paper, a novel Multi-resolution Spatiotemporal Enhanced Transformer Denoising (MSETD) network with an adversarially functional diffusion model is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment (MCI) analysis.
MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis
Coronavirus disease 2019 (COVID-19) has spread all over the world for three years, but medical facilities in many areas still aren't adequate.
Quadratic Graph Attention Network (Q-GAT) for Robust Construction of Gene Regulatory Networks
The former informs that nonlinear aggregation of quadratic neurons can amplify useful signals and suppress unwanted noise, thereby facilitating robustness, while the latter reveals that Q-GAT can leverage more features in prediction thanks to the dual attention mechanism, which endows Q-GAT with the ability to confront adversarial perturbation.
A High-Frequency Focused Network for Lightweight Single Image Super-Resolution
We propose a novel High-Frequency Focused Network (HFFN) through High-Frequency Focused Blocks (HFFBs) that selectively enhance high-frequency information while minimizing redundant feature computation of low-frequency information.
Object Scan Context: Object-centric Spatial Descriptor for Place Recognition within 3D Point Cloud Map
The integration of a SLAM algorithm with place recognition technology empowers it with the ability to mitigate accumulated errors and to relocalize itself.
A RIS-Based Vehicle DOA Estimation Method With Integrated Sensing and Communication System
A novel estimation method is proposed in the scenario with a receiver using only one full-functional channel, where multiple measurements for the DOA estimation are achieved by controlling the reflection matrix (measurement matrix) in the RIS.
Adaptive Zeroing-Type Neural Dynamics for Solving Quadratic Minimization and Applied to Target Tracking
Then the AZTND model is applied to solve the TVQM problem.
Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains
Social media has become a valuable resource for the study of suicidal ideation and the assessment of suicide risk.
Underwater Optical Image Processing: A Comprehensive Review
This paper aims to review the state-of-the-art techniques in underwater image processing by highlighting the contributions and challenges presented in over 40 papers.
