CT images GAN-based augmentation with AdaIN for lung nodules detection

Maksim Kryuchkov; Natalia Khanzhina; Ilya Osmakov; Pavel Ulyanov

doi:10.1117/12.2587940

4 January 2021 CT images GAN-based augmentation with AdaIN for lung nodules detection

Maksim Kryuchkov, Natalia Khanzhina, Ilya Osmakov, Pavel Ulyanov

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 1160526 (2021) https://doi.org/10.1117/12.2587940
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

In this work we made an attempt to improve 3D detection of pulmonary nodules on CT images using Conditional GANs extended with Adaptive Instance Normalization and combined Wasserstein Loss for data augmentation (DA). Nodule generating GAN model used for DA was built upon an open-source CT-GAN network which provides high and reproducible results in the nodule generation task. For the evaluation purpose we used DeepSEED model, which is a 3D end-to-end one-stage detector. We tested our approach on the LUNA16 dataset, the subset of LIDC-IDRI. The proposed model outperformed the baseline detection model trained on the original dataset by 3% of average sensitivity. The augmentation helped achieve a remarkable classification rate: 91% of sensitivity and 86% of specificity.

Citation Download Citation

Maksim Kryuchkov, Natalia Khanzhina, Ilya Osmakov, and Pavel Ulyanov "CT images GAN-based augmentation with AdaIN for lung nodules detection", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 1160526 (4 January 2021); https://doi.org/10.1117/12.2587940

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