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What Is the Role of Annotations in the Detection of Dermoscopic Structures?

  • Bárbara Ferreira
  • Catarina BarataEmail author
  • Jorge S. Marques
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11868)

Abstract

There has been an increasing demand for computer-aided diagnosis systems to become self-explainable. However, in fields such as dermoscopy image analysis this comes at the cost of asking physicians to annotate datasets in a detailed way, such that they simultaneously identify and manually segment regions of medical interest (dermoscopic criteria) in the images. The segmentations are then used to train an automatic detection system to reproduce the procedure. Unfortunately, providing manual segmentations is a cumbersome and time consuming task that may not be generalized to large amounts of data. Thus, this work aims to understand how much information is really needed for a system to learn to detect dermoscopic criteria. In particular, we will show that given sufficient data, it is possible to train a model to detect dermoscopic criteria solely using global annotations at the image level, and achieve similar performances to that of a fully supervised approach, where the model has access to local annotations at the pixel level (segmentations).

Keywords

Skin cancer Dermoscopic structures Supervised model Weakly supervised model corr-LDA 

Notes

Acknowledgments

This work was supported by the FCT project and plurianual funding: [PTDC/EEI PRO/0426/2014], [UID/EEA/50009/2019].

References

  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  2. 2.
    Argenziano, G., Soyer, H.P., De Giorgi, V., et al.: Interactive Atlas of Dermoscopy. EDRA Medical Publishing & New Media (2000)Google Scholar
  3. 3.
    Barata, C., Celebi, M.E., Marques, J.S.: Development of a clinically oriented system for melanoma diagnosis. Pattern Recogn. 69, 270–285 (2017)CrossRefGoogle Scholar
  4. 4.
    Barata, C., Celebi, M.E., Marques, J.S.: A survey of feature extraction in dermoscopy image analysis of skin cancer. IEEE J. Biomed. Health Inform. 23(3), 1096–1109 (2018)CrossRefGoogle Scholar
  5. 5.
    Barata, C., Marques, J.S., Rozeira, J.: A system for the detection of pigment network in dermoscopy images using directional filters. IEEE Trans. Biomed. Eng. 59(10), 2744–2754 (2012)CrossRefGoogle Scholar
  6. 6.
    Blei, D., Jordan, M.: Modeling annotated data. In: 26th Annual International ACM SIGIR Conference on Research and Development in Informataion Retrieval, pp. 127–134. ACM (2003)Google Scholar
  7. 7.
    Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1026–1038 (2002)CrossRefGoogle Scholar
  8. 8.
    Celebi, M.E., Codella, N., Halpern, A.: Dermoscopy image analysis: overview and future directions. IEEE J. Biomed. Health Inform. 23(2), 474–478 (2019)CrossRefGoogle Scholar
  9. 9.
    Codella, N.C.F., Gutman, D., Celebi, M.E., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168–172 (2018)Google Scholar
  10. 10.
    Madooei, A., Drew, M.S., Hajimirsadeghi, H.: Learning to detect blue-white structures in dermoscopy images with weak supervision. IEEE J. Biomed. Health Inform. 23(2), 779–786 (2018)CrossRefGoogle Scholar
  11. 11.
    Oliveira, R., Papa, J., Pereira, A., Tavares, J.: Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Comput. Appl. 29(3), 1–24 (2016).  https://doi.org/10.1007/s00521-016-2482-6CrossRefGoogle Scholar
  12. 12.
    Pathan, S., Prabhu, K.G.S., Siddalingaswamy, P.C.: Techniques and algorithms for computer aided diagnosis of pigmented skin lesions - a review. Biomed. Signal Process. Control 39, 237–262 (2018)CrossRefGoogle Scholar
  13. 13.
    Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2019. CA: Cancer J. Clin. 69, 7–34 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute for Systems and RoboticsInstituto Superior TécnicoLisboaPortugal

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