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Found 7 papers, 0 papers with code
A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning
To address this we introduce a novel embedding constraint based on the Method of Moments (MoM).
Semi-supervised Contrastive Outlier removal for Pseudo Expectation Maximization (SCOPE)
Semi-supervised learning is the problem of training an accurate predictive model by combining a small labeled dataset with a presumably much larger unlabeled dataset.
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.
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.
Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs
Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and volumetric radiomic features.
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.
