Principal component self-attention mechanism for melanoma hyperspectral image recognition
Authors: 
Hong Liang, Nanying Li, Jiaqi Xue, Yaqian Long, Sen JiaAuthors Info & Claims
Hong Liang, Nanying Li, Jiaqi Xue, Yaqian Long, Sen JiaAuthors Info & ClaimsPages 243 - 248
Abstract
Early detection of melanoma and prompt treatment are key approaches to reducing melanoma-related deaths. In order to improve the ability of early detection of melanoma, this paper introduces a set of hyperspectral images (HSIs) data captured by dermoscopy using hyperspectral technology, and based on this data, proposes a principal component self-attention mechanism (PCSAM) method for the classification of dysplastic nevus and melanoma. The proposed method uses principal component analysis technology to amplify the differences in spectral features of the lesions and extract some new features that are convenient for classification. In addition, under the action of the attention mechanism, the spectral features of melanoma are fully paid attention to, and the contextual spatial information between each HSI block can also be utilized. Finally, a comparison experiment is carried out using RGB images and HSIs. Experimental results demonstrate that the spectral features of melanoma can significantly improve the classification accuracy, and it also shows that the participation of hyperspectral technology can effectively improve the recognition accuracy of dysplastic nevus and melanoma, which reflects the advantages of HSI compared with the traditional image.
References
[1]
Naser Alfed and Fouad Khelifi. 2017. Bagged textural and color features for melanoma skin cancer detection in dermoscopic and standard images. Expert Systems with Applications 90, (2017), 101–110.
[2]
Giuseppe Argenziano, Gabriella Fabbrocini, Paolo Carli, Vincenzo De Giorgi, Elena Sammarco, and Mario Delfino. 1998. 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, 12 (1998), 1563–1570.
[3]
Lei Bi, Jinman Kim, Euijoon Ahn, and Dagan Feng. 2017. Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks. arXiv preprint arXiv:1703.04197 (2017).
[4]
Noel CF Codella, David Gutman, M Emre Celebi, Brian Helba, Michael A Marchetti, Stephen W Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, and others. 2018. Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), IEEE, 168–172.
[5]
Noel Codella, Veronica Rotemberg, Philipp Tschandl, M Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, and others. 2019. Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1902.03368 (2019).
[6]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[7]
Terrance DeVries and Dhanesh Ramachandram. 2017. Skin lesion classification using deep multi-scale convolutional neural networks. arXiv preprint arXiv:1703.01402 (2017).
[8]
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, and others. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).
[9]
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun. 2017. Dermatologist-level classification of skin cancer with deep neural networks. nature 542, 7639 (2017), 115–118.
[10]
Yanyang Gu, Yi-Ping Partridge, and Jun Zhou. 2018. A hyperspectral dermoscopy dataset for melanoma detection. In OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis. Springer, 268–276.
[11]
Harold Hotelling. 1933. Analysis of a complex of statistical variables into principal components. Journal of educational psychology 24, 6 (1933), 417.
[12]
Aivars Lorencs, Juris Sinica-Sinavskis, Dainis Jakovels, and Ints Mednieks. 2016. Melanoma-nevus discrimination based on image statistics in few spectral channels. Elektronika ir Elektrotechnika 22, 2 (2016), 66–72.
[13]
Kazuhisa Matsunaga, Akira Hamada, Akane Minagawa, and Hiroshi Koga. 2017. Image classification of melanoma, nevus and seborrheic keratosis by deep neural network ensemble. arXiv preprint arXiv:1703.03108 (2017).
[14]
Teresa Mendonça, Pedro M Ferreira, Jorge S Marques, André RS Marcal, and Jorge Rozeira. 2013. PH 2-A dermoscopic image database for research and benchmarking. In 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE, 5437–5440.
[15]
Franz Nachbar, Wilhelm Stolz, Tanja Merkle, Armand B Cognetta, Thomas Vogt, Michael Landthaler, Peter Bilek, Otto Braun-Falco, and Gerd Plewig. 1994. The ABCD rule of dermatoscopy: high prospective value in the diagnosis of doubtful melanocytic skin lesions. Journal of the American Academy of Dermatology 30, 4 (1994), 551–559.
[16]
T Nagaoka, Y Kiyohara, H Koga, A Nakamura, T Saida, and T Sota. 2015. Modification of a melanoma discrimination index derived from hyperspectral data: a clinical trial conducted in 2 centers between March 2011 and December 2013. Skin Research and Technology 21, 3 (2015), 278–283.
[17]
Takashi Nagaoka, Atsushi Nakamura, Haruka Okutani, Yoshio Kiyohara, Hiroshi Koga, Toshiaki Saida, and Takayuki Sota. 2013. Hyperspectroscopic screening of melanoma on acral volar skin. Skin Research and Technology 19, 1 (2013), e290–e296.
[18]
Takashi Nagaoka, Atsushi Nakamura, Haruka Okutani, Yoshio Kiyohara, and Takayuki Sota. 2012. A possible melanoma discrimination index based on hyperspectral data: A pilot study. Skin Research and Technology 18, 3 (2012), 301–310.
[19]
Paolo Roma, Imma Savarese, Antonia Martino, Domenico Martino, Pietro Annese, Patrizio Capoluongo, Ines Mordente, Rachele Nicolino, Iris Zalaudek, and Giuseppe Argenziano. 2007. Slow-growing melanoma: report of five cases. Journal of Dermatological Case Reports 1, 1 (2007), 1.
[20]
Margarida Ruela, Catarina Barata, Jorge S Marques, and Jorge Rozeira. 2017. A system for the detection of melanomas in dermoscopy images using shape and symmetry features. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 5, 2 (2017), 127–137.
[21]
TY Satheesha, D Satyanarayana, MN Giri Prasad, and Kashyap D Dhruve. 2017. Melanoma is skin deep: a 3D reconstruction technique for computerized dermoscopic skin lesion classification. IEEE journal of translational engineering in health and medicine 5, (2017), 1–17.
[22]
Rebecca L Siegel, Kimberly D Miller, and Ahmedin Jemal. 2019. Cancer statistics, 2019. CA: a cancer journal for clinicians 69, 1 (2019), 7–34.
[23]
H Peter Soyer, Giuseppe Argenziano, Iris Zalaudek, Rosamaria Corona, Francesco Sera, Renato Talamini, Filomena Barbato, Adone Baroni, Lorenza Cicale, Alessandro Di Stefani, and others. 2004. Three-point checklist of dermoscopy. Dermatology 208, 1 (2004), 27–31.
[24]
Stig Uteng, Eduardo Quevedo, Gustavo M. Callico, Irene Castaño, Gregorio Carretero, Pablo Almeida, Aday Garcia, Javier A. Hernandez, and Fred Godtliebsen. 2021. Curve-Based Classification Approach for Hyperspectral Dermatologic Data Processing. Sensors 21, 3 (2021), 680.
[25]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30, (2017).
[26]
Zhen Yu, Jennifer Nguyen, Toan D Nguyen, John Kelly, Catriona Mclean, Paul Bonnington, Lei Zhang, Victoria Mar, and Zongyuan Ge. 2021. Early Melanoma Diagnosis with Sequential Dermoscopic Images. IEEE Transactions on Medical Imaging 41, 3 (2021), 633–646.
[27]
Zhen Yu, Dong Ni, Siping Chen, Jin Qin, Shengli Li, Tianfu Wang, and Baiying Lei. 2017. Hybrid dermoscopy image classification framework based on deep convolutional neural network and Fisher vector. In 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), IEEE, 301–304.
[28]
Larisa A Zherdeva, Ivan A Bratchenko, Oleg O Myakinin, Alexander A Moryatov, Sergey V Kozlov, and Valery P Zakharov. 2016. In vivo hyperspectral imaging and differentiation of skin cancer. In Optics in Health Care and Biomedical Optics VII, SPIE, 658–665.
Index Terms
- Principal component self-attention mechanism for melanoma hyperspectral image recognition
Recommendations
A Hyperspectral Dermoscopy Dataset for Melanoma Detection
OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image AnalysisAbstractMelanoma is the most fatal type of skin cancer. Non-invasive melanoma detection is crucial for preliminary screening and early diagnosis. Among various image based techniques, hyperspectral imaging is a tool with great potential for melanoma ...
Automated malignant melanoma detection using MATLAB
DNCOCO'06: Proceedings of the 5th WSEAS international conference on Data networks, communications and computersMalignant melanoma, the most deadly form of skin cancer, has a good prognosis if treated in the curable early stages. Early diagnosis and surgical excision is the most effective treatment of melanoma. Well-trained dermatologists reach a high level of ...
Unsupervised detection of density changes through principal component analysis for lung lesion classification
Lung cancer remains one of the most common cancers worldwide. Temporal evaluation is a useful tool for analyzing the malignant behavior of a lesion during treatment or that of indeterminate lesions which may be benign. Thereby, this work proposes a ...
Comments
Information & Contributors
Information
Published In
November 2022
683 pages
ISBN:9781450397056
DOI:10.1145/3581807
Copyright © 2022 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].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 May 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Shenzhen Science and Technology Program
- The Key Project of Department of Education of Guangdong Province
- The National Natural Science Foundation of China
- The Guangdong Basic and Applied Basic Research Foundation
Conference
ICCPR 2022
ICCPR 2022: 2022 11th International Conference on Computing and Pattern Recognition
November 17 - 19, 2022
Beijing, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 43Total Downloads
- Downloads (Last 12 months)30
- Downloads (Last 6 weeks)2
Reflects downloads up to 15 Oct 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format