Search Results for author:
Found 9 papers, 0 papers with code
Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications
Federated Learning (FL) has revolutionized how we train deep neural networks by enabling decentralized collaboration while safeguarding sensitive data and improving model performance.
Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports
Rapid and accurate identification of Venous thromboembolism (VTE), a severe cardiovascular condition including deep vein thrombosis (DVT) and pulmonary embolism (PE), is important for effective treatment.
Soft Merging: A Flexible and Robust Soft Model Merging Approach for Enhanced Neural Network Performance
This merging process not only enhances the model performance by converging to a better local optimum, but also minimizes computational costs, offering an efficient and explicit learning process integrated with stochastic gradient descent.
CCS-GAN: COVID-19 CT-scan classification with very few positive training images
CCS-GAN achieves high accuracy with few positive images and thereby greatly reduces the barrier of acquiring large training volumes in order to train a diagnostic classifier for COVID-19.
Tolerating Adversarial Attacks and Byzantine Faults in Distributed Machine Learning
In addition, Byzantine faults including software, hardware, network issues occur in distributed systems which also lead to a negative impact on the prediction outcome.
Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network
We present a novel conditional Generative Adversarial Network (cGAN) architecture that is capable of generating 3D Computed Tomography scans in voxels from noisy and/or pixelated approximations and with the potential to generate full synthetic 3D scan volumes.
Deep Expectation-Maximization for Semi-Supervised Lung Cancer Screening
We present a semi-supervised algorithm for lung cancer screening in which a 3D Convolutional Neural Network (CNN) is trained using the Expectation-Maximization (EM) meta-algorithm.
Generating Realistic COVID19 X-rays with a Mean Teacher + Transfer Learning GAN
We present a novel Mean Teacher + Transfer GAN (MTT-GAN) that generates COVID19 chest X-ray images of high quality.
Hybrid Mortality Prediction using Multiple Source Systems
The use of artificial intelligence in clinical care to improve decision support systems is increasing.
