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2021 | OriginalPaper | Buchkapitel

Don’t Tear Your Hair Out: Analysis of the Impact of Skin Hair on the Diagnosis of Microscopic Skin Lesions

verfasst von : Alessio Gallucci, Dmitry Znamenskiy, Nicola Pezzotti, Milan Petkovic

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

Recent work on the classification of microscopic skin lesions does not consider how the presence of skin hair may affect diagnosis. In this work, we investigate how deep-learning models can handle a varying amount of skin hair during their predictions. We present an automated processing pipeline that tests the performance of the classification model. We conclude that, under realistic conditions, modern day classification models are robust to the presence of skin hair and we investigate three architectural choices (Resnet50, InceptionV3, Densenet121) that make them so.

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Vorheriges Kapitel UIP-Net: A Decoder-Encoder CNN for the Detection and Quantification of Usual Interstitial Pneumoniae Pattern in Lung CT Scan Images

Nächstes Kapitel Deep Learning Based Segmentation of Breast Lesions in DCE-MRI

Stern, R.S.: Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch. Dermatol. 146(3), 279–282 (2010)CrossRef

Guy Jr, G.P., Machlin, S.R., Ekwueme, D.U., Yabroff, K.R.: Prevalence and costs of skin cancer treatment in the US, 2002–2006 and 2007–2011. Am. J. Prev. Med. 48(2), 183–187 (2015)CrossRef

Smith, R.A., et al.: Cancer screening in the United States, 2018: a review of current American Cancer Society guidelines and current issues in cancer screening. CA. Cancer J. Clin. 68(4), 297–316 (2018)CrossRef

Krizhevsky, A., Sutskever, I., Hinton, G.E.: 2012 AlexNet. Adv. Neural Inf. Process. Syst. 1–9 (2012). https://doi.org/10.1016/j.protcy.2014.09.007

Brinker, T.J., et al.: A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. Eur. J. Cancer 111, 148–154 (2019). https://doi.org/10.1016/j.ejca.2019.02.005CrossRef

Huang, A., Kwan, S.-Y., Chang, W.-Y., Liu, M.-Y., Chi, M.-H., Chen, G.-S.: A robust hair segmentation and removal approach for clinical images of skin lesions. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3315–3318 (2013)

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

Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018)CrossRef

Combalia, M., et al.: BCN20000: Dermoscopic lesions in the wild. arXiv Preprint arXiv:1908.02288 (2019)

10.

Codella, N., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC), p. 12 (2019). https://arxiv.org/abs/1902.03368

11.

Chowis (2019). https://chowis.com/hair-ai-diagnostic-technology/

12.

Hoffmann, R.: TrichoScan Ein neues Werkzeug fr die digitale Haarzhlung. Der Hautarzt 12(53), 798–804 (2002)CrossRef

13.

Vallotton, P., Thomas, N.: Automated body hair counting and length measurement. Skin Res. Technol. 14(4), 493–497 (2008). https://doi.org/10.1111/j.1600-0846.2008.00322.xCrossRef

14.

Shih, H.-C.: An unsupervised hair segmentation and counting system in microscopy images. IEEE Sens. J. 15(6), 3565–3572 (2014)CrossRef

15.

Shih, H.-C., Lin, B.-S.: Hair segmentation and counting algorithms in microscopy image. In: 2015 IEEE International Conference on Consumer Electronics (ICCE), pp. 612–613 (2015)

16.

Lim, H., et al.: Development of a novel automated hair counting system for the quantitative evaluation of laser hair removal. Photomed. Laser Surg. 35(2), 116–121 (2017). https://doi.org/10.1089/pho.2016.4140CrossRef

17.

Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRef

18.

Buda, M., Saha, A., Mazurowski, M.A.: Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. Comput. Biol. Med. 109, 218–225 (2019)CrossRef

19.

Sudre, C., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 240–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_28CrossRef

20.

Iglovikov, V., Mushinskiy, S., Osin, V.: Satellite imagery feature detection using deep convolutional neural network: a Kaggle competition. arXiv Preprint arXiv:1706.06169 (2017)

21.

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)

22.

Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)CrossRef

23.

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)

24.

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)

25.

Perez, F., Vasconcelos, C., Avila, S., Valle, E.: Data augmentation for skin lesion analysis. In: Stoyanov, D., et al. (eds.) CARE/CLIP/OR 2.0/ISIC -2018. LNCS, vol. 11041, pp. 303–311. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01201-4_33CrossRef

26.

Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-yMathSciNetCrossRef

27.

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv Preprint arXiv:1412.6980 (2014)

28.

Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

29.

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)

Titel: Don’t Tear Your Hair Out: Analysis of the Impact of Skin Hair on the Diagnosis of Microscopic Skin Lesions
verfasst von: Alessio Gallucci
Dmitry Znamenskiy
Nicola Pezzotti
Milan Petkovic
Verlag: Springer International Publishing
Buch: Pattern Recognition. ICPR International Workshops and Challenges
Print ISBN: 978-3-030-68762-5

Electronic ISBN: 978-3-030-68763-2

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-68763-2_31

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