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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 …