CCS-GAN: COVID-19 CT-scan classification with very few positive training images

no code implementations • 1 Oct 2021 • Sumeet Menon, Jayalakshmi Mangalagiri, Josh Galita, Michael Morris, Babak Saboury, Yaacov Yesha, Yelena Yesha, Phuong Nguyen, Aryya Gangopadhyay, David Chapman

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.

Generative Adversarial Network Style Transfer +1

Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network

no code implementations • 2 Apr 2021 • Jayalakshmi Mangalagiri, David Chapman, Aryya Gangopadhyay, Yaacov Yesha, Joshua Galita, Sumeet Menon, Yelena Yesha, Babak Saboury, Michael Morris, Phuong Nguyen

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.

Denoising Generative Adversarial Network +1

Generating Realistic COVID19 X-rays with a Mean Teacher + Transfer Learning GAN

no code implementations • 26 Sep 2020 • Sumeet Menon, Joshua Galita, David Chapman, Aryya Gangopadhyay, Jayalakshmi Mangalagiri, Phuong Nguyen, Yaacov Yesha, Yelena Yesha, Babak Saboury, Michael Morris

We present a novel Mean Teacher + Transfer GAN (MTT-GAN) that generates COVID19 chest X-ray images of high quality.

Image Classification Transfer Learning