Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation

Gündel S, Setio A, Grbic S, Maier A, Comaniciu D (2020)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Publisher: Springer

Pages Range: 504 - 512

Conference Proceedings Title: Machine Learning in Medical Imaging

ISBN: 978-3-030-59861-7

DOI: 10.1007/978-3-030-59861-7_51

Abstract

Chest X-ray (CXR) is the most common examination for fast detection of pulmonary abnormalities. Recently, automated algorithms have been developed to classify multiple diseases and abnormalities in CXR scans. However, because of the limited availability of scans containing nodules and the subtle properties of nodules in CXRs, state-of-the-art methods do not perform well on nodule classification. To create additional data for the training process, standard augmentation techniques are applied. However, the variance introduced by these methods are limited as the images are typically modified globally. In this paper, we propose a method for local feature augmentation by extracting local nodule features using a generative inpainting network. The network is applied to generate realistic, healthy tissue and structures in patches containing nodules. The nodules are entirely removed in the inpainted representation. The extraction of the nodule features is processed by subtraction of the inpainted patch from the nodule patch. With arbitrary displacement of the extracted nodules in the lung area across different CXR scans and further local modifications during training, we significantly increase the nodule classification performance and outperform state-of-the-art augmentation methods.

Authors with CRIS profile

Sebastian Gündel Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens Corporate Research US United States (USA) (US) Siemens Healthineers DE Germany (DE)

How to cite

APA:

Gündel, S., Setio, A., Grbic, S., Maier, A., & Comaniciu, D. (2020). Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation. In Liu M., Yan P., Lian C., Cao X. (Eds.), Machine Learning in Medical Imaging (pp. 504 - 512). Springer.

MLA:

Gündel, Sebastian, et al. "Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation." Proceedings of the 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2019 Ed. Liu M., Yan P., Lian C., Cao X., Springer, 2020. 504 - 512.

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