WYBRANE ZASTOSOWANIA GŁĘBOKICH SIECI NEURONOWYCH W DIAGNOZIE ZMIAN SKÓRNYCH
Magdalena Michalska
mmagamichalska@gmail.comPolitechnika Lubelska, Katedra Elektroniki i Technik Informacyjnych (Polska)
http://orcid.org/0000-0002-0874-3285
Abstrakt
Artykuł zawiera przegląd wybranych zastosowań głębokich sieci neuronowych w diagnostyce zmian skórnych z obrazów dermatoskopowych człowieka z uwzględnieniem wielu chorób dermatologicznych, w tym bardzo niebezpiecznej z nich malignant melanoma. Został opisany proces segmentacji zmiany, selekcji cech i klasyfikacji. Uwzględniono przykłady binarnej i wieloklasowej klasyfikacji. Opisane algorytmy znalazły szerokie zastosowanie w diagnostyce zmian skórnych. Porównano i przeanalizowano skuteczność, specyficzność i dokładność klasyfikatorów w oparciu o dostępne zestawy danych.
Słowa kluczowe:
obrazy dermatoskopowe, sztuczne sieci neuronowe, melanoma, zmiany skórneBibliografia
Adegun A., Viriri S.: Deep learning-based system for automatic melanoma detection. IEEE Access 8/2020, 7160-7172.
DOI: https://doi.org/10.1109/ACCESS.2019.2962812
Google Scholar
Alendar F. et al.: Clear definitions,simple terminology, no metaphoric terms. Expert RevDermatol 3/2008, 27–29.
DOI: https://doi.org/10.1586/17469872.3.1.27
Google Scholar
Argenziano G. et al.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Archives of Dermatology 134/1998, 1563–1570.
DOI: https://doi.org/10.1001/archderm.134.12.1563
Google Scholar
Argenziano G. et al.: Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. Journal of American Academy of Dermatology 48(5)/2003, 679–693.
Google Scholar
Attia M. et al.: Skin melanoma segmentation using recurrent and convolutional neural networks. Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium, IEEE, 292–296.
DOI: https://doi.org/10.1109/ISBI.2017.7950522
Google Scholar
Blum H., Ellwanger U.: Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions, British Journal of Dermatology 151(5)/2004, 1029–1038.
DOI: https://doi.org/10.1111/j.1365-2133.2004.06210.x
Google Scholar
Brinker T.J. et al.: Deep learning outperformed 136 of 157 dermatologists in a head-to-head der moscopic melanoma image classification task. Eur J Cancer 113/2019, 47–54.
Google Scholar
Codella N. et al.: Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. Machine Learning in Medical Imaging 2015, 118–126.
DOI: https://doi.org/10.1007/978-3-319-24888-2_15
Google Scholar
Codella N. et al.: Deep learning ensembles for melanoma recognition in dermoscopy images 2016, http://arxiv.org/abs/1610.04662 (accessed 4 October 2019).
Google Scholar
Esteva A. et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542/2017, 115–118.
Google Scholar
Esteva A.: Dermatologist-level classification of skin cancer with deep neural networks. Nat. Res. 542(7639)/2017, 115–118,.
DOI: https://doi.org/10.1038/nature21056
Google Scholar
Ge Y. et al.: Melanoma seg-mentation and classification in clinical images using deep learning. ICMLC 2018: Proceedings of the 2018 10th International Conference on Machine Learning and Computing 2018, 252–256.
DOI: https://doi.org/10.1145/3195106.3195164
Google Scholar
Ge Z. et al.: Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images. Springer, Cham LNCS 10435/2017, 250–258.
DOI: https://doi.org/10.1007/978-3-319-66179-7_29
Google Scholar
Haenssle H.A. et al.: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 29/2018, 1836–1342.
DOI: https://doi.org/10.1093/annonc/mdy520
Google Scholar
Haenssle H.A. et al.: Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermato-logists working under less artificial conditions. Ann Oncol 31/2020, 137–143.
Google Scholar
He K., Zhang X, Ren, S., Sun J.: Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition 2016, 770–778.
DOI: https://doi.org/10.1109/CVPR.2016.90
Google Scholar
Hekler A. et al.: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. Eur J Cancer 118/2019, 91–96.
DOI: https://doi.org/10.1016/j.ejca.2019.06.012
Google Scholar
Kittler H. et al.: Dermatoscopy of unpigmented lesions of the skin: A new classification of vessel morphology based on pattern analysis. Dermatopathology: Practical & Conceptual 14(4)/2008, 3.
Google Scholar
Li Y., Shen L.: Skin lesion analysis towards melanoma detection using deep learning network. Sensors 18/2018, 556.
DOI: https://doi.org/10.3390/s18020556
Google Scholar
Lopez A. R. et al.: Skin lesion classification from dermatoscopic images using deep learning techniques.
Google Scholar
Maia L. et al.: Evaluation of melanoma diagnosis using deep features, 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP), 1–4, 2018.
DOI: https://doi.org/10.1109/IWSSIP.2018.8439373
Google Scholar
Marchetti M. A. et al.: Results of the 2016 international skin imaging collaboration international symposium on biomedical imaging challenge: comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. J Am Acad Dermatol 78/2018, 270–277.
DOI: https://doi.org/10.1016/j.jaad.2017.08.016
Google Scholar
Marchetti M.A. et al: Computer algorithms show potential for improving dermatologists’ accuracy to diagnose cutaneous melanoma: results of the international skin imaging collaboration 2017. J Am Acad Dermatol 82/2020, 622–627.
DOI: https://doi.org/10.1016/j.jaad.2019.07.016
Google Scholar
Maron R.C. et al.: Systematic outperformance of 112 dermato-logists in multiclass skin cancer image classification by convo-lutional neural networks. Eur J Cancer 119/2019, 57–65.
Google Scholar
Menzies S. et al.: Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features. Archives of Dermatology 132/1996, 1178–1182.
DOI: https://doi.org/10.1001/archderm.132.10.1178
Google Scholar
Murphree D. H. et al.: Deep learning for dermatologists: Part I. J Am Acad Dermatol 2020, 1–9.
Google Scholar
Nida N. et al.: Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. International Journal of Medical Informatics 124/2019, 37–48.
DOI: https://doi.org/10.1016/j.ijmedinf.2019.01.005
Google Scholar
Nijeweme-d’Hollosy W. et al.: Evaluation of three machine learning models for self-referral decision support on low back pain in primary care. Int. J. Med. Inform. 110/2018, 31–41.
DOI: https://doi.org/10.1016/j.ijmedinf.2017.11.010
Google Scholar
Phillips M. et al.: Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions. JAMA Netw Open 2/2019, 1913436.
DOI: https://doi.org/10.1001/jamanetworkopen.2019.13436
Google Scholar
Rosendahl C., Cameron A., McColl I., Wilkinson I.: Dermatoscopy in routine practice, Chaos and Clues. Australian Family Physician 41(7)/2012.
Google Scholar
Simonyan K., Zisserman A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
Google Scholar
Szegedy C. et al.: Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition 2015, 1–9.
DOI: https://doi.org/10.1109/CVPR.2015.7298594
Google Scholar
Tschandl P et al.: Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, webbased, international, diagnostic study. Lancet Oncol 2019(20)/2019, 938–947.
DOI: https://doi.org/10.1016/S1470-2045(19)30333-X
Google Scholar
Wang Y. et al.: Incorporating clinical knowledge with constrained classifier chain into a multimodal deep network for melanoma detection. Computers in Biology and Medicine 137/2021, 104812.
DOI: https://doi.org/10.1016/j.compbiomed.2021.104812
Google Scholar
Young A. T. et al.: Artificial intelligence in dermatology: A Primer. Journal of Investigative Dermatology 140/2020, 1504–1512.
DOI: https://doi.org/10.1016/j.jid.2020.02.026
Google Scholar
Yu L. et al.: Automated melano-ma recognition in dermoscopy images via very deep residual networks. IEEE transactions on medical imaging 36(4)/2017, 994–1004.
DOI: https://doi.org/10.1109/TMI.2016.2642839
Google Scholar
Zhang J. et al.: Skin lesion classification in dermoscopy images using synergic deep learning, Springer Nature Switzerland, LNCS 11071/2018, 12–20.
DOI: https://doi.org/10.1007/978-3-030-00934-2_2
Google Scholar
Zhang X.: Melanoma segmentation based on deep learning. Computer Assisted Surgery 22/2017, 267–277.
DOI: https://doi.org/10.1080/24699322.2017.1389405
Google Scholar
Autorzy
Magdalena Michalskammagamichalska@gmail.com
Politechnika Lubelska, Katedra Elektroniki i Technik Informacyjnych Polska
http://orcid.org/0000-0002-0874-3285
Statystyki
Abstract views: 293PDF downloads: 190
Licencja
Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa – Na tych samych warunkach 4.0 Miedzynarodowe.