Abstract
Cervical tissue ablation is an effective treatment approach for excising high-grade precancerous lesions, which are a direct precursor to invasive cervical cancer. However, not all women are eligible for this ablative treatment due to their cervical characteristics. In our previous study, we presented a deep learning network that used pyramidal features to determine if a cervix is eligible for ablative treatment based on visual characteristics presented in the image. Our method demonstrated promising performance and valid visualization in the task of “treatability classification”. In this work, we propose using an image augmenter followed by a customized classification convolutional neural network (CNN) to overcome the challenges due to insufficient training data. We build the image augmenter using a CycleGAN model that is trained using three different datasets to ensure that the augmented images contain clinically significant morphological features. A gynecologic oncologist with more than 20 years of experience validated the augmented images. These are mixed into the set of original images to train our customized CNN. We note a performance improvement of 3.3% (to 89.8%) in treatability classification. We believe that a similar technique can also be applied to other automatic image classification applications for cervical cancer screening.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
World Health Organization: Human papillomavirus (HPV) and cervical cancer. World Health Organization, 24 January 2019. https://www.who.int/en/news-room/fact-sheets/detail/human-papillomavirus-(hpv)-and-cervical-cancer. Accessed 24 Feb 21
Jeronimo, J., Schiffman, M.: Colposcopy at a crossroads. Am. J. Obstet. Gynecol 195(6), 349–353 (2006)
World Health Organization: WHO Guidelines for the Use of Thermal Ablation for Cervical Pre-Cancer Lesions. World Health Organization (2019). https://www.who.int/reproductivehealth/publications/thermal-ablation-for-cervical-pre-cancer-lesions/en/. Accessed 24 Feb 21
Guo, P., et al.: Network visualization and pyramidal feature comparison for ablative treatability classification using digitized cervix images. J. Clin. Med. 10(5), 953 (2021)
Bratti, M., Rodriguez, A., Schiffman, M., et al.: Description of a seven-year prospective study of human papillomavirus infection and cervical neoplasia among 10000 women in Guanacaste. Costa Rica. Rev. Panam. Salud Publica 15(2), 75–89 (2004)
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019). https://doi.org/10.1186/s40537-019-0197-0
Zhu, J., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2242–2251 (2017). https://doi.org/10.1109/ICCV.2017.244
Goodfellow, I., et al.: Generative adversarial networks. In: Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014) (2014)
Jordan, J., Singer, A., Jones, H., Shafi, M.: The Cervix. Wiley, Hoboken (2009)
Gage, J.C., et al.: Treatability by cryotherapy in a screen-and-treat strategy. J. Low. Genit. Tract Dis. 13, 172–181 (2009)
The Atypical Squamous Cells of Undetermined Significance/Low-Grade Squamous Intraepithelial Lesions Triage Study (ALTS) Group: Human papillomavirus testing for triage of women with cytologic evidence of low-grade squamous intraepithelial lesions: baseline data from a randomized trial. J. Nat. Cancer Inst. 92, 397–402 (2000)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp. 2999–3007 (2017)
Kim, I., Rajaraman, S., Antani, S.: Visual interpretation of convolutional neural network predictions in classifying medical image modalities. Diagnostics 9(2), 38 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply
About this paper
Cite this paper
Guo, P. et al. (2022). Image Augmentation for Improving Automated Eligibility-Classification for Cervical Precancer Ablation Treatment. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-07005-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-07004-4
Online ISBN: 978-3-031-07005-1
eBook Packages: Computer ScienceComputer Science (R0)