Improved Generative Model for Weakly Supervised Chest Anomaly Localization via Pseudo-paired Registration with Bilaterally Symmetrical Data Augmentation

by   Kyung-Su Kim, et al.

Image translation based on a generative adversarial network (GAN-IT) is a promising method for precise localization of abnormal regions in chest X-ray images (AL-CXR). However, heterogeneous unpaired datasets undermine existing methods to extract key features and distinguish normal from abnormal cases, resulting in inaccurate and unstable AL-CXR. To address this problem, we propose an improved two-stage GAN-IT involving registration and data augmentation. For the first stage, we introduce an invertible deep-learning-based registration technique that virtually and reasonably converts unpaired data into paired data for learning registration maps. This novel approach achieves high registration performance. For the second stage, we apply data augmentation to diversify anomaly locations by swapping the left and right lung regions on the uniform registered frames, further improving the performance by alleviating imbalance in data distribution showing left and right lung lesions. Our method is intended for application to existing GAN-IT models, allowing existing architecture to benefit from key features for translation. By showing that the AL-CXR performance is uniformly improved when applying the proposed method, we believe that GAN-IT for AL-CXR can be deployed in clinical environments, even if learning data are scarce.


page 13

page 14

page 18

page 19

page 20

page 22

page 23

page 24


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

Recent works show that Generative Adversarial Networks (GANs) can be suc...

Generative Image Translation for Data Augmentation of Bone Lesion Pathology

Insufficient training data and severe class imbalance are often limiting...

A multi-stage GAN for multi-organ chest X-ray image generation and segmentation

Multi-organ segmentation of X-ray images is of fundamental importance fo...

Contralaterally Enhanced Networks for Thoracic Disease Detection

Identifying and locating diseases in chest X-rays are very challenging, ...

TBI-GAN: An Adversarial Learning Approach for Data Synthesis on Traumatic Brain Segmentation

Brain network analysis for traumatic brain injury (TBI) patients is crit...

Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space

Increased drone proliferation in civilian and professional settings has ...

Please sign up or login with your details

Forgot password? Click here to reset