MDVA-GAN: multi-domain visual attribution generative adversarial networks

ORCID Identifiers


Document Type


Source of Publication

Neural Computing and Applications

Publication Date



Some pixels of an input image have thick information and give insights about a particular category during classification decisions. Visualization of these pixels is a well-studied problem in computer vision, called visual attribution (VA), which helps radiologists to recognize abnormalities and identify a particular disease in the medical image. In recent years, several classification-based techniques for domain-specific attribute visualization have been proposed, but these techniques can only highlight a small subset of most discriminative features. Therefore, their generated VA maps are inadequate to visualize all effects in an input image. Due to recent advancements in generative models, generative model-based VA techniques are introduced which generate efficient VA maps and visualize all affected regions. To deal the issue, generative adversarial network-based VA techniques are recently proposed, where the researchers leverage the advances in domain adaption techniques to learn a map for abnormal-to-normal medical image translation. As these approaches rely on a two-domain translation model, it would require training as many models as number of diseases in a medical dataset, which is a tedious and compute-intensive task. In this work, we introduce a unified multi-domain VA model that generates a VA map of more than one disease at a time. The proposed unified model gets images from a particular domain and its domain label as input to generate VA map and visualize all the affected regions by that particular disease. Experiments on the CheXpert dataset, which is a publicly available multi-disease chest radiograph dataset, and the TBX11K dataset show that the proposed model generates identical results.


Springer Science and Business Media LLC


Computer Sciences


Abnormal-to-normal translation, Change map, Chest X-ray, Generative adversarial network, Tuberculosis, Visual attribution

Scopus ID


Indexed in Scopus


Open Access