Self-Transfer Learning for Weakly Supervised Lesion Localization

  • Sangheum HwangEmail author
  • Hyo-Eun Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)


Recent advances of deep learning have achieved remarkable performances in various computer vision tasks including weakly supervised object localization. Weakly supervised object localization is practically useful since it does not require fine-grained annotations. Current approaches overcome the difficulties of weak supervision via transfer learning from pre-trained models on large-scale general images such as ImageNet. However, they cannot be utilized for medical image domain in which do not exist such priors. In this work, we present a novel weakly supervised learning framework for lesion localization named as self-transfer learning (STL). STL jointly optimizes both classification and localization networks to help the localization network focus on correct lesions without any types of priors. We evaluate STL framework over chest X-rays and mammograms, and achieve significantly better localization performance compared to previous weakly supervised localization approaches.


Weakly supervised learning Lesion localization Convolutional neural networks 


  1. 1.
    Candemir, S., et al.: Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans. Med. Imaging 33(2), 577–590 (2014)CrossRefGoogle Scholar
  2. 2.
    Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40763-5_51 CrossRefGoogle Scholar
  3. 3.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009)Google Scholar
  4. 4.
    Heath, M., Bowyer, K., Kopans, D., Kegelmeyer Jr., P., Moore, R., Chang, K., Munishkumaran, S.: Current status of the digital database for screening mammography. In: Proceedings of the Fourth International Workshop on Digital Mammography, pp. 457–460 (1998)Google Scholar
  5. 5.
    Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, W.P.: The digital database for screening mammography. In: Proceedings of the 5th international workshop on digital mammography, pp. 212–218 (2000)Google Scholar
  6. 6.
    Hwang, S., Kim, H.E., Jeong, J., Kim, H.J.: A novel approach for tuberculosis screening based on deep convolutional neural networks. In: Proceedings of SPIE Medical Imaging (2016)Google Scholar
  7. 7.
    Jaeger, S., Karargyris, A., Candemir, S., Siegelman, J., Folio, L., Antani, S., Thoma, G.: Automatic screening for tuberculosis in chest radiographs: a survey. Quant. Imaging Med. Surg. 3(2), 89–99 (2013)Google Scholar
  8. 8.
    Jaeger, S., et al.: Automatic tuberculosis screening using chest radiographs. IEEE Trans. Med. Imaging 33(2), 233–245 (2014)CrossRefGoogle Scholar
  9. 9.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)Google Scholar
  10. 10.
    Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Is object localization for free?-weakly-supervised learning with convolutional neural networks. In: CVPR, pp. 685–694 (2015)Google Scholar
  11. 11.
    Pinheiro, P.O., Collobert, R.: From image-level to pixel-level labeling with convolutional networks. In: CVPR, pp. 1713–1721 (2015)Google Scholar
  12. 12.
    Roth, H.R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E.B., Summers, R.M.: DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 556–564. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24553-9_68 CrossRefGoogle Scholar
  13. 13.
    Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S., et al.: The mammographic image analysis society digital mammogram database. Exerpta Medica Int. Cong. Ser. 1069, 375–378 (1994)Google Scholar
  14. 14.
    Wu, J., Yu, Y., Huang, C., Yu, K.: Deep multiple instance learning for image classification and auto-annotation. In: CVPR, pp. 3460–3469 (2015)Google Scholar
  15. 15.
    Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. arXiv preprint (2015). arXiv:1512.04150

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Lunit Inc.SeoulKorea

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