research-article

Explainable Fully Connected Visual Words for the Classification of Skin Cancer Confocal Images: Interpreting the influence of visual words in classifying benign vs malignant pattern

Authors:

Athanasios Kallipolitis,

Alexandros Stratigos,

Alexios Zarras,

Ilias MaglogiannisAuthors Info & Claims

SETN 2020: 11th Hellenic Conference on Artificial Intelligence

Pages 67 - 73

https://doi.org/10.1145/3411408.3411435

Published: 02 September 2020 Publication History

Abstract

Skin cancer is affecting the lives of million people worldwide. Early detection and treatment of the cause can reduce drastically morbidity. Although the main workflow in dermatology clinics includes invasive skin removal procedures for diagnostic purposes, Reflectance Confocal Microscopy (RCM) provides an ancillary, non-invasive methodology for reviewing areas of interest of the human skin at a high resolution. In this paper, we propose a method for the classification and the interpretation of visual patterns in skin cancer confocal images. Both tasks are based on the formation of a visual vocabulary from Speeded up Robust Features (SURF) and the utilization of simple shallow artificial neural network with fully connected layers. Interpretability of the predictive models is also quite important, since it improves their reliability, accountability, transparency and provides useful insight of how to evolve the predictive model towards better performance. The paper discusses the technical details of both approaches along with some initial results.

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Athanasios Kallipolitis, Alexandros Stratigos, Alexios Zarras and Ilias Maglogiannis, 2020. Fully Connected Visual Words for the Classification of Skin Cancer Confocal Images. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Volume 5, 853-858.

Cited By

Myslicka MKawala-Sterniuk ABryniarska ASudol APodpora MGasz RMartinek RKahankova Vilimkova RVilimek DPelc MMikolajewski D(2024)Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposesArchives of Dermatological Research10.1007/s00403-024-02828-1316:4Online publication date: 6-Mar-2024
https://doi.org/10.1007/s00403-024-02828-1
Kallipolitis AYfantis PMaglogiannis I(2023)Improving explainability results of convolutional neural networks in microscopy imagesNeural Computing and Applications10.1007/s00521-023-08452-w35:29(21535-21553)Online publication date: 21-Mar-2023
https://doi.org/10.1007/s00521-023-08452-w
Vindhya JPooja CDongre MGowrishankar SSrinivasa AVeena A(2023)Skin Lesion Classification Using Machine LearningInventive Computation and Information Technologies10.1007/978-981-19-7402-1_27(385-397)Online publication date: 2-Mar-2023
https://doi.org/10.1007/978-981-19-7402-1_27
Show More Cited By

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cover image ACM Other conferences

SETN 2020: 11th Hellenic Conference on Artificial Intelligence

September 2020

249 pages

ISBN:9781450388788

DOI:10.1145/3411408

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 02 September 2020

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European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation

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SETN 2020

SETN 2020: 11th Hellenic Conference on Artificial Intelligence

September 2 - 4, 2020

Athens, Greece

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Cited By

Myslicka MKawala-Sterniuk ABryniarska ASudol APodpora MGasz RMartinek RKahankova Vilimkova RVilimek DPelc MMikolajewski D(2024)Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposesArchives of Dermatological Research10.1007/s00403-024-02828-1316:4Online publication date: 6-Mar-2024
https://doi.org/10.1007/s00403-024-02828-1
Kallipolitis AYfantis PMaglogiannis I(2023)Improving explainability results of convolutional neural networks in microscopy imagesNeural Computing and Applications10.1007/s00521-023-08452-w35:29(21535-21553)Online publication date: 21-Mar-2023
https://doi.org/10.1007/s00521-023-08452-w
Vindhya JPooja CDongre MGowrishankar SSrinivasa AVeena A(2023)Skin Lesion Classification Using Machine LearningInventive Computation and Information Technologies10.1007/978-981-19-7402-1_27(385-397)Online publication date: 2-Mar-2023
https://doi.org/10.1007/978-981-19-7402-1_27
Kallipolitis ATziomaka MPapadopoulos DMaglogiannis I(2022)Explainable computer vision analysis for embryo selection on blastocyst images2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)10.1109/BHI56158.2022.9926740(1-4)Online publication date: 27-Sep-2022
https://doi.org/10.1109/BHI56158.2022.9926740
Tziomaka MKallipolitis ATsanakas PMaglogiannis I(2021)Evaluating Mental Patients Utilizing Video Analysis of Facial ExpressionsArtificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops10.1007/978-3-030-79157-5_16(182-193)Online publication date: 22-Jun-2021
https://doi.org/10.1007/978-3-030-79157-5_16

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