Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation

1 code implementation • 11 Oct 2019 • Yunyan Xing, ZongYuan Ge, Rui Zeng, Dwarikanath Mahapatra, Jarrel Seah, Meng Law, Tom Drummond

We demonstrate the effectiveness of our model on two tasks: (i) we invite certified radiologists to assess the quality of the generated synthetic images against real and other state-of-the-art generative models, and (ii) data augmentation to improve the performance of disease localisation.

Data Augmentation Image-to-Image Translation +1

Generative Visual Rationales

no code implementations • 4 Apr 2018 • Jarrel Seah, Jennifer Tang, Andy Kitchen, Jonathan Seah

For each prediction, we generate visual rationales by optimizing a latent representation to minimize the prediction of disease while constrained by a similarity measure in image space.

Thinking like a machine — generating visual rationales through latent space optimization

no code implementations • ICLR 2018 • Jarrel Seah, Jennifer Tang, Andy Kitchen, Jonathan Seah

For each prediction, we generate visual rationales for positive classifications by optimizing a latent representation to minimize the probability of disease while constrained by a similarity measure in image space.

Deep Generative Adversarial Neural Networks for Realistic Prostate Lesion MRI Synthesis

no code implementations • 1 Aug 2017 • Andy Kitchen, Jarrel Seah

Generative Adversarial Neural Networks (GANs) are applied to the synthetic generation of prostate lesion MRI images.