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.

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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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