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

HyunWoo Kim, HanBin Ko, JungJun Kim
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, PMLR 172:676-688, 2022.

Abstract

he 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 the 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. and quantitative results on the RSNA-pneumonia dataset

Cite this Paper

BibTeX
@InProceedings{pmlr-v172-kim22a, title = {PILLET-GAN: Pixel-Level Lesion Traversal Generative Adversarial Network for Pneumonia Localization}, author = {Kim, HyunWoo and Ko, HanBin and Kim, JungJun}, booktitle = {Proceedings of The 5th International Conference on Medical Imaging with Deep Learning}, pages = {676--688}, year = {2022}, editor = {Konukoglu, Ender and Menze, Bjoern and Venkataraman, Archana and Baumgartner, Christian and Dou, Qi and Albarqouni, Shadi}, volume = {172}, series = {Proceedings of Machine Learning Research}, month = {06--08 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v172/kim22a/kim22a.pdf}, url = {https://proceedings.mlr.press/v172/kim22a.html}, abstract = {he 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 the 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. and quantitative results on the RSNA-pneumonia dataset} }
Endnote
%0 Conference Paper %T PILLET-GAN: Pixel-Level Lesion Traversal Generative Adversarial Network for Pneumonia Localization %A HyunWoo Kim %A HanBin Ko %A JungJun Kim %B Proceedings of The 5th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2022 %E Ender Konukoglu %E Bjoern Menze %E Archana Venkataraman %E Christian Baumgartner %E Qi Dou %E Shadi Albarqouni %F pmlr-v172-kim22a %I PMLR %P 676--688 %U https://proceedings.mlr.press/v172/kim22a.html %V 172 %X he 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 the 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. and quantitative results on the RSNA-pneumonia dataset
APA
Kim, H., Ko, H. & Kim, J.. (2022). PILLET-GAN: Pixel-Level Lesion Traversal Generative Adversarial Network for Pneumonia Localization. Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 172:676-688 Available from https://proceedings.mlr.press/v172/kim22a.html.

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