Search Results for author:
Found 19 papers, 6 papers with code
StarWhisper Telescope: Agent-Based Observation Assistant System to Approach AI Astrophysicist
Additionally, the integration of AI agents within the system provides online accessibility, saving astronomers' time and encouraging greater participation from amateur astronomers in the NGSS project.
A Narrative Review of Image Processing Techniques Related to Prostate Ultrasound
In consequence, this survey provides a \textcolor{blue}{narrative } analysis of this field, outlining the evolution of image processing methods in the context of TRUS image analysis and meanwhile highlighting their relevant contributions.
Towards Multi-modality Fusion and Prototype-based Feature Refinement for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound
In this study, we propose a novel learning framework for clinically significant prostate cancer (csPCa) classification using multi-modality TRUS.
Efficient Multi-View Fusion and Flexible Adaptation to View Missing in Cardiovascular System Signals
The progression of deep learning and the widespread adoption of sensors have facilitated automatic multi-view fusion (MVF) about the cardiovascular system (CVS) signals.
Multi-modality transrectal ultrasound video classification for identification of clinically significant prostate cancer
With the aim of effectively identifying prostate cancer, we propose a framework for the classification of clinically significant prostate cancer (csPCa) from multi-modality TRUS videos.
A Prototype-Based Neural Network for Image Anomaly Detection and Localization
This paper proposes ProtoAD, a prototype-based neural network for image anomaly detection and localization.
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning
To tune different components for DBMS, a coordinating mechanism is needed to make the multiple agents cognizant of each other.
End-To-End Audiovisual Feature Fusion for Active Speaker Detection
After fusion, one BiGRU layer is attached to model the joint temporal dynamics.
Feature Aggregation and Refinement Network for 2D AnatomicalLandmark Detection
In this paper, we propose a novel deep network, named feature aggregation and refinement network (FARNet), for the automatic detection of anatomical landmarks.
Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation
We leverage the pixel-level L2 loss and the pixel contrastive loss for the two purposes respectively.
Full-Resolution Encoder-Decoder Networks with Multi-Scale Feature Fusion for Human Pose Estimation
To achieve more accurate 2D human pose estimation, we extend the successful encoder-decoder network, simple baseline network (SBN), in three ways.
Semantically Robust Unpaired Image Translation for Data with Unmatched Semantics Statistics
Many applications of unpaired image-to-image translation require the input contents to be preserved semantically during translations.
Efficient Human Pose Estimation with Depthwise Separable Convolution and Person Centroid Guided Joint Grouping
Two branches of sub-networks are used to predict the centroids, body joints and their offsets to their parent nodes.
Automated Segmentation of Brain Gray Matter Nuclei on Quantitative Susceptibility Mapping Using Deep Convolutional Neural Network
Abnormal iron accumulation in the brain subcortical nuclei has been reported to be correlated to various neurodegenerative diseases, which can be measured through the magnetic susceptibility from the quantitative susceptibility mapping (QSM).
MODRL/D-AM: Multiobjective Deep Reinforcement Learning Algorithm Using Decomposition and Attention Model for Multiobjective Optimization
Each subproblem is modeled with a pointer network and the model is trained with reinforcement learning.
Hybrid Channel Based Pedestrian Detection
When using a more advanced RPN in our framework, our approach can be further improved and get competitive results on both benchmarks.
Automatic acute ischemic stroke lesion segmentation using semi-supervised learning
By using a large number of weakly labeled subjects and a small number of fully labeled subjects, our proposed method is able to accurately detect and segment the AIS lesions.
Towards Clinical Diagnosis: Automated Stroke Lesion Segmentation on Multimodal MR Image Using Convolutional Neural Network
The patient with ischemic stroke can benefit most from the earliest possible definitive diagnosis.
Training Auto-encoders Effectively via Eliminating Task-irrelevant Input Variables
Auto-encoders are often used as building blocks of deep network classifier to learn feature extractors, but task-irrelevant information in the input data may lead to bad extractors and result in poor generalization performance of the network.
