PILLET-GAN: Pixel-Level Lesion Traversal Generative Adversarial Network for Pneumonia LocalizationDownload PDF

Published: 28 Feb 2022, Last Modified: 16 May 2023MIDL 2022Readers: Everyone
Keywords: Medical image analysis, Generatvie adversarial network, Pnemonia localization, Computer-aided system
TL;DR: We propose to localize the Pneumonia disease via image-to-image translation where our proposed network efficiently collects the meaningful category attributes and fuses them into target domain features to generate a clear localized image.
Abstract: The study of pneumonia localization focus on the problem of accurate lesion localization in the thoracic X-ray image. It is crucial to provide precisely localized regions to users. It can lay out the basis of the model decision by comparing the X-ray image between the `Healthy' and `Disease' classes. In particular, for the medical image analysis, it is essential not only to make a correct prediction for the disease but also to provide evidence to support accurate predictions. Many generative adversarial networks (GAN) based approaches are employed to show the pixel-level changes via domain translation technique to address this issue. Although previous research tried to improve localization performance by understanding the domain's attributes for better image translation, it remains challenging to capture the specific category's pixel-level changes. For this reason, we focus on the stage of understanding of category attributes. We propose a Pixel-Level Lesion Traversal Generative Adversarial Network (PILLET-GAN) that mines spatial features for the category via spatial attention technique and fuses them into an original feature map extracted from the generator for better domain translation. Our experimental results show that PILLET-GAN achieves superior performance compared to the state-of-the-art models on qualitative and quantitative results on the RSNA-pneumonia dataset.
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Paper Type: methodological development
Primary Subject Area: Application: Radiology
Secondary Subject Area: Interpretability and Explainable AI
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