research-article

DC-SMIL: a multiple instance learning solution via spherical separation for automated detection of displastyc nevi

Authors:

Eugenio Vocaturo,

Giovanni Giallombardo,

Giovanna MiglionicoAuthors Info & Claims

IDEAS '20: Proceedings of the 24th Symposium on International Database Engineering & Applications

Article No.: 4, Pages 1 - 9

https://doi.org/10.1145/3410566.3410611

Published: 25 August 2020 Publication History

Abstract

Among skin cancers, melanoma is the most aggressive and most lethal form. Despite these terrible premises, an excision treatment carried out thanks to an early diagnosis is almost always decisive, guaranteeing the patient's survival. The early detection of melanoma is hampered by the extreme similarity of melanoma with other skin lesions such as dysplastic nevi. The current research is aimed at defining software solutions that support the computerized diagnosis of lesions for the detection of melanoma. To date, the proposals, both in terms of algorithms and frameworks, have focused on the dichotomous distinction of melanoma from benign lesions. However, the current debate on Dysplastic Nevi Syndrome (DNS), makes issues relating to the nature of the lesions, central to subjects who present a large number of moles throughout the body. In fact, individuals with DNS have a greater chance of being attacked by melanoma. The classification task relating to the distinction of dysplastic nevi from common ones is totally unexplored. In this document, we consider the difficult task of applying multiple-instance learning (MIL) approaches to discriminate melanoma from dysplastic nevi and outline an even more complex challenge related to the classification of dysplastic nevi from common ones. In particular, we introduce the application of a MIL approach that uses spherical separation surfaces. Since the results seem promising, we conclude that a MIL technique could be the basis of more sophisticated tools useful for detecting skin lesions.

References

[1]

http://gco.iarc.fr/today/explore.

[2]

Silva JH, de Sa B, Avila A, Landman G, Duprat Neto JP., "Atypical mole syndrome and dysplastic nevi: identification of populations at risk for developing melanoma," Clinics (Sao Paulo), v.66(3):493âĂŞ9, 2011.

[3]

A.C. Society, Melanoma skin cancer. http://www.cancer.org/acs/groups/cid/documents/webcontent/003120-pdf.pdf

[4]

Burroni M, Sbano P, Cevenini G, Risulo M, DellâĂ&Zacute;Eva G, Barbini P, et al. "Dysplastic naevus vs. in situ melanoma: digital dermoscopy analysis". Br J Dermatol, v.152(4), pp:679âĂŞ84, 2005.

[5]

, Aly M., "Survey on multiclass classification methods", Neural Netw, v. 19, pp. 1--9, 2005.

[6]

Gaudioso, M., Giallombardo, G., Miglionico, G., and Vocaturo, E., "Classification in the multiple instance learning framework via spherical separation". Soft Computing, pp:1--7, 2019.

[7]

Duffy K, Grossman D., "The dysplastic nevus: from historical perspective to management in the modern era: part I. Historical, histologic, and clinical aspects". J Am Acad Dermatol., 67(1):1.e1-1.e16. quiz 17-8, 2012.

[8]

Greene, M. H. and Clark Jr, Wallace H. and Tucker, M. A. and Kraemer, K. H. and Elder, D. E. and Fraser, M. C., "High risk of malignant melanoma in melanoma-prone families with dysplastic nevi". Annals of internal medicine, 102(4), pp: 458--465, 1985.

[9]

Jeong, D. K., Bae, Y. C., Lee, S. J., Kim, H. S., and Choi, Y. J., "A case of malignant melanoma after repeated recurrent dysplastic nevi". Archives of craniofacial surgery, 20(4), 260, 2019.

[10]

Save, S., "Dysplastic Nevi", Dermoscopy: Text and Atlas, 447, 2019.

[11]

Marghoob, A., and Braun, R., "An atlas of dermoscopy". CRC Press, 2012.

[12]

R. Pampena, A. Kyrgidis, A. Lallas, E. Moscarella, G. Argenziano, C. Longo, "A meta-analysis of nevus-associated melanoma: Prevalence and practical implications". In: Journal of the American Academy of Dermatology. Band 77, Nummer 5, pp. 938âĂŞ945, 2017.

[13]

Arumi-Uria M, McNutt NS, Finnerty B., "Grading of atypia in nevi: correlation with melanoma risk". Mod Pathol., 16(8):764--771, 2003.

[14]

Reddy KK, Farber MJ, Bhawan J, Geronemus RG, Rogers GS., "Atypical (dysplastic) nevi: outcomes of surgical excision and association with melanoma". JAMA Dermatol., 149(8): 928--934, 2013.

[15]

Rieger E, Soyer HP, Garbe C, et al., "Overall and site-specific risk of malignant melanoma associated with nevus counts at different body sites: a multicenter case-control study of the German Central Malignant-Melanoma Registry". Int J Cancer. 62(4):393--397, 1995.

[16]

Gandini S, Sera F, Cattaruzza MS, Pasquini P, Abeni D, Boyle P, Melchi CF, "Meta-analysis of risk factors for cutaneous melanoma: I. Common and atypical nevi". Eur J Cancer., 41(1):28--44, 2005.

[17]

M Y Xiong, M S Rabkin, M W Piepkorn, R L Barnhill, Z Argenyi, L Erickson, J Guitart, L Lowe, C R Shea, M J Trotter, R A Lew, M A Weinstock, "Diameter of dysplastic nevi is amore robust biomarker of increased melanoma risk than degree of histologic dysplasia: a case-control study". J Am Acad Dermatol., 71(6):1257--1258.e4, 2014.

[18]

Rastgoo, M., Garcia, R., Morel, O., and Marzani, "Automatic differentiation of melanoma from dysplastic nevi". Computerized Medical Imaging and Graphics, 43, 44--52, 2015.

[19]

Barata C, Marques JS, Rozeira J., "The role of keypoint sampling on the classification of melanomas in dermoscopy images using bag-of-features". In: Pattern recognition and image analysis. Springer, p. 715âĂŞ23, 2013.

[20]

Quellec G, Cazuguel G, Cochener B, Lamard M, "Multiple instance learning for medical image and video analysisâĂİ, IEEE Rev Biomed Eng 10, pp:213âĂŞ234, 2017.

[21]

Astorino, A., Fuduli, A., Veltri, P., and Vocaturo, E., "On a recent algorithm for multiple instance learning. Preliminary applications in image classification". In IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1615--1619, 2017.

[22]

Astorino, A., Fuduli, A., Gaudioso, M., and Vocaturo, E., "A multiple instance learning algorithm for color images classification". In Proceedings of the 22nd International Database Engineering and Applications Symposium (pp. 262--266). ACM, 2018.

Digital Library

[23]

Astorino, A., Fuduli, A., Veltri, P., and Vocaturo, E. (2019). Melanoma detection by means of Multiple Instance Learning. Interdisciplinary Sciences: Computational Life Sciences, 1--8, 2019.

[24]

Litjens, G. and Kooi, T. and Bejnordi, B. E. and Setio, A. A. A. and Ciompi, F. and Ghafoorian, M. and Van Der Laak, J. A. and Van Ginneken, B. and Sánchez, C. I., "A survey on deep learning in medical image analysisâĂİ. Med. Image Anal. 42, 60âĂŞ88, 2017.

[25]

Weese, J. and Lorenz, C., 2016. "Four challenges in medical image analysis from an industrial perspectiveâĂİ. Med. Image Anal. 33, 44âĂŞ49, 2016.

[26]

de Bruijne, M., âĂIJMachine learning approaches in medical image analysis: from detection to diagnosisâĂİ. Med. Image Anal. 33, 94âĂŞ97, 2016.

[27]

Amores J., "Multiple instance classification: review, taxonomy and comparative studyâĂİ. Artificial Intelligence 201:81âĂŞ105, 2013.

Digital Library

[28]

Carbonneau M.A., Cheplygina V., Granger E., Gagnon G., "Multiple instance learning: a survey of problem characteristics and applicationsâĂİ. Pattern Recogn 77:329âĂŞ353, 2018.

Digital Library

[29]

de Oliveira, W., "Proximal bundle methods for nonsmooth DC programming", Journal of Global Optimization, pp.1--41, 2019.

[30]

Gaudioso, M., Giallombardo, G., Miglionico, G., "Minimizing piecewise-concave functions over polyhedra", Mathematics of Operations Research, vol.43, n. 2, pp. 580--59, 2017.

[31]

Gaudioso, M., Giallombardo, G., Miglionico, G., and Bagirov Adil M, "Minimizing nonsmooth DC functions via successive DC piecewise-affine approximations", Journal of Global Optimization, v. 71, n. 1, pp. 37--55, 2018.

Digital Library

[32]

Joki, K., Bagirov, Adil M., Karmitsa, N. and Makela, Marko M, "A proximal bundle method for nonsmooth DC optimization utilizing nonconvex cutting planes", Journal of Global Optimization, v.68, n. 3, pp. 501--535, 2017.

Digital Library

[33]

Joki K., Bagirov A.M., Karmitsa N., MakelÃd' M.M. and Taheri S., "Double bundle method for finding Clarke stationary points in nonsmooth DC programming", SIAM Journal on Optimization, v. 28, n. 2, pp. 1892--1919, 2018.

Digital Library

[34]

Schumacher M., Holländer N., Sauerbrei W., "Resampling and cross-validation techniques: a tool to reduce bias caused by model building", Statistics in medicine, v.16, n. 24, pp. 2813--2827, Wiley Online Library, 1997.

[35]

MendonÃğa T., Ferreira P.M., Marques J.S., Marcal A.R.S., Rozeira J., "Ph2 - a dermoscopic image database for research and benchmarking". In: 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 5437âĂŞ5440, 2013.

[36]

Dietterich T.G., Lathrop R.H., Lozano-PÃl'rez T., "Solving the multiple instance problem with axis-parallel rectanglesâĂİ, Artificial Intelligence 89(1âĂŞ2): pp. 31âĂŞ71, 1997.

[37]

Andrews S., Tsochantaridis I., Hofmann T., "Support vector machines for multiple-instance learningâĂİ, Advances in neural information processing systems, pp. 577--584, 2003.

[38]

Carson, C. and Thomas, M. and Belongie, S. and Hellerstein, J. M. and Malik, J., "Blobworld: A system for region-based image indexing and retrievalâĂİ, International conference on advances in visual information systems, pp. 509--517, 1999.

[39]

Sanghera R. and Grewal P. S., "Dermatological symptom assessmentâĂİ, in Patient Assessment in Clinical Pharmacy, p. 133âĂŞ154, Springer, 2019.

[40]

Barata C, Ruela M, Francisco M, MendoncÂ&Yuml; a T, Marques J. Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst J 8(3), pp: 965âĂŞ79, 2014.

[41]

Astorino, A., Fuduli, A., and Gaudioso, M., "A Lagrangian relaxation approach for binary multiple instance classification". IEEE transactions on neural networks and learning systems, 2019.

[42]

A. Astorino, A. Fuduli, G. Giallombardo, G. Miglionico, "SVM-Based Multiple Instance Classification via DC Optimization", Algorithms, v. 12, n.12, pp. 249, 2019.

[43]

Vapnik V., "The nature of the statistical learning theoryâĂİ, Springer, New York 1995.

[44]

E. Vocaturo, and E. Zumpano, and P. Veltri, "On the Usefulness of Pre-Processing Step in Melanoma Detection Using Multiple Instance LearningâĂİ, International Conference on Flexible Query Answering Systems, Springer, pp. 374--382, 2019.

[45]

E. Vocaturo, and E. Zumpano, and P. Veltri, "Image preprocessing in computer vision systems for melanoma detection,âĂİ IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2117--24, 2018.

[46]

E. Vocaturo, and E. Zumpano, and P. Veltri, "Features for Melanoma Lesions Characterization in Computer Vision SystemsâĂIJ, 9th International Conference on Information, Intelligence, Systems and Applications, (IISA) 2018, Zakynthos, Greece, July 23-25, pp. 1--8, 2018.

[47]

Vocaturo E., Caroprese L., Zumpano E., "Features for Melanoma Lesions: Extraction and Classification", WI âĂ&Zacute;19 Companion, October 14âĂŞ17, 2019, Thessaloniki, Greece in Press, 2019.

Digital Library

[48]

E. Vocaturo, E. Zumpano, P. Veltri, "On discovering relevant features for tongue colored image analysis", IDEAS 2019: 12:1--12:8

[49]

A. Astorino, A. Fuduli, M. Gaudioso, and E. Vocaturo, "Multiple Instance Learning Algorithm for Medical Image ClassificationâĂİ, Proceedings of the 27th Italian Symposium on Advanced Database (SEDB), 2019.

[50]

A. Fuduli, P. Veltri, E. Vocaturo, E. Zumpano, "Melanoma detection using color and texture features in computer vision systems", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 16--22 (2019).

[51]

E. Zumpano, P. Iaquinta, L. Caroprese, G.L. Cascini, F. Dattola, P. Franco, M. Iusi, P. Veltri, E. Vocaturo, "SIMPATICO 3D: A Medical Information System for Diagnostic Procedures", BIBM pp. 2125--2128, 2018.

[52]

E. Zumpano, P. Iaquinta, F. Dattola, L. Caroprese, G. Tradigo, P. Veltri, E. Vocaturo, "SIMPATICO 3D Mobile for Diagnostic Procedures", IIWAS 2019, in press.

[53]

E. Vocaturo, P. Veltri, "On the use of Networks in Biomedicine", FNC/MobiSPC 2017: 498--503.

[54]

Vocaturo E., and Zumpano E., and Veltri P. "On discovering relevant features for tongue colored image analysis", Proceedings of the 23rd International Database Applications & Engineering Symposium, IDEAS, Athens, Greece, June 10-12, 2019, pp. 1--8.

Digital Library

Cited By

Malik FYousaf MSial HViriri S(2024)Exploring dermoscopic structures for melanoma lesions' classificationFrontiers in Big Data10.3389/fdata.2024.13663127Online publication date: 25-Mar-2024
https://doi.org/10.3389/fdata.2024.1366312
Avolio MFuduli AVocaturo EZumpano E(2023)On Detection of Diabetic Retinopathy via Multiple Instance LearningProceedings of the 27th International Database Engineered Applications Symposium10.1145/3589462.3589490(170-176)Online publication date: 5-May-2023
https://dl.acm.org/doi/10.1145/3589462.3589490
Das DErgin EMorel BNoga MEmery DPunithakumar K(2023)AI-assisted mole detection for online dermatology triage in telemedicine settingsInformatics in Medicine Unlocked10.1016/j.imu.2023.10131141(101311)Online publication date: 2023
https://doi.org/10.1016/j.imu.2023.101311
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Index Terms

DC-SMIL: a multiple instance learning solution via spherical separation for automated detection of displastyc nevi
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Information systems
  1. Data management systems
  2. Information systems applications
    1. Decision support systems
      1. Expert systems

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cover image ACM Other conferences

IDEAS '20: Proceedings of the 24th Symposium on International Database Engineering & Applications

August 2020

252 pages

ISBN:9781450375030

DOI:10.1145/3410566

General Chairs:
Bipin C. Desai
Concordia University, Canada
,
Wan-Sup Cho
Chungbuk National University, Korea

Copyright © 2020 ACM.

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Published: 25 August 2020

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IDEAS 2020

IDEAS 2020: 24th International Database Engineering & Applications Symposium

August 12 - 14, 2020

Seoul, Republic of Korea

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IDEAS '20 Paper Acceptance Rate 27 of 57 submissions, 47%;

Overall Acceptance Rate 74 of 210 submissions, 35%

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Cited By

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https://doi.org/10.3389/fdata.2024.1366312
Avolio MFuduli AVocaturo EZumpano E(2023)On Detection of Diabetic Retinopathy via Multiple Instance LearningProceedings of the 27th International Database Engineered Applications Symposium10.1145/3589462.3589490(170-176)Online publication date: 5-May-2023
https://dl.acm.org/doi/10.1145/3589462.3589490
Das DErgin EMorel BNoga MEmery DPunithakumar K(2023)AI-assisted mole detection for online dermatology triage in telemedicine settingsInformatics in Medicine Unlocked10.1016/j.imu.2023.10131141(101311)Online publication date: 2023
https://doi.org/10.1016/j.imu.2023.101311
Suiçmez ÇTolga Kahraman HSuiçmez AYılmaz CBalcı F(2023)Detection of melanoma with hybrid learning method by removing hair from dermoscopic images using image processing techniques and wavelet transformBiomedical Signal Processing and Control10.1016/j.bspc.2023.10472984(104729)Online publication date: Jul-2023
https://doi.org/10.1016/j.bspc.2023.104729
Vocaturo EZumpano E(2023)Assembling Fragments of Ancient Papyrus via Artificial IntelligencePervasive Knowledge and Collective Intelligence on Web and Social Media10.1007/978-3-031-31469-8_1(3-13)Online publication date: 28-Apr-2023
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Vizza PBarrese CMarafioti LCascini GVeltri P(2022)Medical image fusion: a proposed methodology for treatment evaluation2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9995560(2954-2956)Online publication date: 6-Dec-2022
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Cevallos MDe Biase MVocaturo EZumpano E(2022)Fake News Detection on COVID 19 tweets via Supervised Learning Approach2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9994918(2765-2772)Online publication date: 6-Dec-2022
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Fuduli AGaudioso MKhalaf WVocaturo E(2022)A heuristic approach for multiple instance learning by linear separationSoft Computing10.1007/s00500-021-06713-126:7(3361-3368)Online publication date: 17-Jan-2022
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Manzo MPellino S(2020)Bucket of Deep Transfer Learning Features and Classification Models for Melanoma DetectionJournal of Imaging10.3390/jimaging61201296:12(129)Online publication date: 26-Nov-2020
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