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2021 | OriginalPaper | Chapter

Crop It, but Not Too Much: The Effects of Masking on the Classification of Melanoma Images

Authors : Fabrizio Nunnari, Abraham Ezema, Daniel Sonntag

Published in: KI 2021: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

To improve the accuracy of convolutional neural networks in discriminating between nevi and melanomas, we test nine different combinations of masking and cropping on three datasets of skin lesion images (ISIC2016, ISIC2018, and MedNode). Our experiments, confirmed by 10-fold cross-validation, show that cropping increases classification performances, but specificity decreases when cropping is applied together with masking out healthy skin regions. An analysis of Grad-CAM saliency maps shows that in fact our CNN models have the tendency to focus on healthy skin at the border when a nevus is classified.

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Berseth, M.: ISIC 2017 - skin lesion analysis towards melanoma detection. CoRR abs/1703.00523 (2017). http://arxiv.org/abs/1703.00523

Bissoto, A., Fornaciali, M., Valle, E., Avila, S.: (De)constructing bias on skin lesion datasets. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019

Burdick, J., Marques, O., Weinthal, J., Furht, B.: Rethinking skin lesion segmentation in a convolutional classifier. J. Digit. Imaging 31(4), 435–440 (2017). https://doi.org/10.1007/s10278-017-0026-yCrossRef

Codella, N., Rotemberg, V., Tschandl, P., Celebi, M.E., et al.: Skin lesion analysis toward melanoma detection 2018 (2019). http://arxiv.org/abs/1902.03368

Codella, N.C.F., Gutman, D., Celebi, M.E., Helba, B., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 International Symposium on Biomedical Imaging. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168–172. IEEE, Washington, DC, April 2018. https://doi.org/10.1109/ISBI.2018.8363547

Curiel-Lewandrowski, C., Novoa, R.A., Berry, E., Celebi, M.E., et al.: Artificial Intelligence Approach in Melanoma. In: Fisher, D., Bastian, B. (eds.) Melanoma, pp. 1–31. Springer, New York (2019). https://doi.org/10.1007/978-1-4614-7322-0_43-1CrossRef

Deng, J., Dong, W., Socher, R., Li, L.J., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE, Miami, June 2009. https://doi.org/10.1109/CVPR.2009.5206848

Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115 (2017). https://doi.org/10.1038/nature21056CrossRef

Giotis, I., Molders, N., Land, S., Biehl, M., et al.: MED-NODE: a computer-assisted melanoma diagnosis system using non-dermoscopic images. Expert Syst. Appl. 42(19), 6578–6585 (2015). https://doi.org/10.1016/j.eswa.2015.04.034CrossRef

10.

ISIC: International Skin Imaging Collaboration. https://www.isic-archive.com/

11.

Kawahara, J., Hamarneh, G.: Visual diagnosis of dermatological disorders: human and machine performance, June 2019. http://arxiv.org/abs/1906.01256

12.

Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates, Inc. (2012)

13.

Marchetti, M.A., Codella, N.C., Dusza, S.W., Gutman, D.A., et al.: Results of the 2016 international skin imaging collaboration international symposium on biomedical imaging challenge. J. Am. Acad. Dermatol. 78(2), 270-277.e1 (2018). https://doi.org/10.1016/j.jaad.2017.08.016CrossRef

14.

Masood, A., Ali Al-Jumaily, A.: Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int. J. Biomed. Imaging 2013, 1–22 (2013). https://doi.org/10.1155/2013/323268CrossRef

15.

Nguyen, D.M.H., Ezema, A., Nunnari, F., Sonntag, D.: A visually explainable learning system for skin lesion detection using multiscale input with attention U-net. In: Schmid, U., Klügl, F., Wolter, D. (eds.) KI 2020. LNCS (LNAI), vol. 12325, pp. 313–319. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58285-2_28CrossRef

16.

Nunnari, F., Sonntag, D.: A software toolbox for deploying deep learning decision support systems with XAI capabilities. In: Proceedings of the 13th ACM SIGCHI Symposium on Engineering Interactive Computing Systems. ACM (2021). https://doi.org/10.1145/3459926.3464753

17.

Petsiuk, V., Das, A., Saenko, K.: RISE: randomized input sampling for explanation of black-box models. In: Proceedings of the British Machine Vision Conference (BMVC) (2018)

18.

Qian, C., Liu, T., Jiang, H., Wang, Z., et al.: A detection and segmentation architecture for skin lesion segmentation on dermoscopy images. CoRR abs/1809.03917 (2018). http://arxiv.org/abs/1809.03917

19.

Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28CrossRef

20.

Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., et al.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: The IEEE International Conference on Computer Vision (ICCV), October 2017

21.

Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2019. CA: Cancer J. Clin. 69(1), 7–34 (2019). https://doi.org/10.3322/caac.21551CrossRef

22.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition, September 2014. http://arxiv.org/abs/1409.1556

23.

Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 270–279. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01424-7_27CrossRef

24.

Wahlster, W., Winterhalter, C.: German standardization roadmap on artificial intelligence. Technical report, DIN e.V. and German Commission for Electrical, Electronic & Information Technologies of DIN and VDE (2020)

25.

Winkler, J.K., Fink, C., Toberer, F., Enk, A., et al.: Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA Dermatol. 155(10), 1135 (2019). https://doi.org/10.1001/jamadermatol.2019.1735CrossRef

Title: Crop It, but Not Too Much: The Effects of Masking on the Classification of Melanoma Images
Authors: Fabrizio Nunnari
Abraham Ezema
Daniel Sonntag
Publisher: Springer International Publishing
Book: KI 2021: Advances in Artificial Intelligence
Print ISBN: 978-3-030-87625-8

Electronic ISBN: 978-3-030-87626-5

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-87626-5_13

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