Abstract
One of the most challenging aspects of medical image analysis is the lack of a high quantity of annotated data. This makes it difficult for deep learning algorithms to perform well due to a lack of variations in the input space. While generative adversarial networks have shown promise in the field of synthetic data generation, but without a carefully designed prior the generation procedure can not be performed well. In the proposed approach we have demonstrated the use of automatically generated segmentation masks as learnable class-specific priors to guide a conditional GAN for the generation of patho-realistic samples for cytology image. We have observed that augmentation of data using the proposed pipeline called “SynCGAN” improves the performance of state of the art classifiers such as ResNet-152, DenseNet-161, Inception-V3 significantly.
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References
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017)
Bissoto, A., Perez, F., Valle, E., Avila, S.: Skin lesion synthesis with generative adversarial networks. In: Stoyanov, D., et al. (eds.) CARE/CLIP/OR 2.0/ISIC -2018. LNCS, vol. 11041, pp. 294–302. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01201-4_32
Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: Gan-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321, 321–331 (2018)
Ghosh, S., Das, N., Das, I.S., Maulik, U.: Understanding deep learning techniques for image segmentation. ArXiv abs/1907.06119 (2019)
Ghosh, S., Das, N., Nasipuri, M.: Reshaping inputs for convolutional neural network: some common and uncommon methods. Pattern Recogn. 93, 79–94 (2019)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Kowal, M., Filipczuk, P., Obuchowicz, A., Korbicz, J.: Computer-aided diagnosis of breast cancer using gaussian mixture cytological image segmentation. J. Med. Inf. Technol. 17 (2011)
Kumar, S., Gupta, M.D.: C+GAN: complementary fashion item recommendation. arXiv preprint arXiv:1906.05596 (2019)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Shin, H.-C., et al.: Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In: Gooya, A., Goksel, O., Oguz, I., Burgos, N. (eds.) SASHIMI 2018. LNCS, vol. 11037, pp. 1–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00536-8_1
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Tom, F., Sheet, D.: Simulating patho-realistic ultrasound images using deep generative networks with adversarial learning. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1174–1177. IEEE (2018)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Acknowledgement
This work is funded by SERB (DST), Govt. of India (Ref no. EEQ/2018/000963). The authors are thankful to Theism Medical Diagnostics Centre, Kolkata, West Bengal, India for providing cytology samples and also thanks to Centre for Microprocessor Application for Training, Education, and Research, Jadavpur University for providing additional infrastructure for the research.
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Dey, S., Das, S., Ghosh, S., Mitra, S., Chakrabarty, S., Das, N. (2020). SynCGAN: Using Learnable Class Specific Priors to Generate Synthetic Data for Improving Classifier Performance on Cytological Images. In: Babu, R.V., Prasanna, M., Namboodiri, V.P. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2019. Communications in Computer and Information Science, vol 1249. Springer, Singapore. https://doi.org/10.1007/978-981-15-8697-2_3
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