Search Results for author:
Found 6 papers, 1 papers with code
A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning
Computer aided diagnostic (CAD) tools are developed to help radiologists to compensate for some of these errors.
Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning
Transrectal ultrasound (US) is the most commonly used imaging modality to guide prostate biopsy and its 3D volume provides even richer context information.
Multi-Domain Image Completion for Random Missing Input Data
Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e. g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI).
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.
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.
Voice EHR: Introducing Multimodal Audio Data for Health
This report introduces a consortium of partners for global work, presents the application used for data collection, and showcases the potential of informative voice EHR to advance the scalability and diversity of audio AI.
