Title

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

ORCID Identifiers

0000-0001-8176-3373

Document Type

Article

Source of Publication

Neural Computing and Applications

Publication Date

1-1-2022

Abstract

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.

Publisher

Springer Science and Business Media LLC

Disciplines

Computer Sciences

Keywords

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

Scopus ID

85123921344

Indexed in Scopus

yes

Open Access

no

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