{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T01:56:16Z","timestamp":1743126976086,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031232350"},{"type":"electronic","value":"9783031232367"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-23236-7_5","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T01:22:43Z","timestamp":1672536163000},"page":"65-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Attention Mechanism for\u00a0Classification of\u00a0Melanomas"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8092-5630","authenticated-orcid":false,"given":"C\u00e1tia","family":"Loureiro","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3747-6577","authenticated-orcid":false,"given":"V\u00edtor","family":"Filipe","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6573-7511","authenticated-orcid":false,"given":"Lio","family":"Gon\u00e7alves","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","unstructured":"Shahana sherin, K. C, Shayini, R.: Classification of skin lesions in digital images for the diagnosis of skin cancer. In: 2020 International Conference on Smart Electronics and Communication (ICOSEC), pp. 162\u2013166. IEEE, India (2020). https:\/\/doi.org\/10.1109\/ICOSEC49089.2020.9215271","DOI":"10.1109\/ICOSEC49089.2020.9215271"},{"key":"5_CR2","unstructured":"2020 Melanoma Skin Cancer Report: Stemming The Global Epidemic. https:\/\/www.melanomauk.org.uk\/2020-melanoma-skin-cancer-report. Accessed 20 Jul 2022"},{"key":"5_CR3","unstructured":"Barata, A.C.F.: Automatic detection of melanomas using dermoscopy images. Technical report, Instituto Superior Tecnico Lisboa (2017)"},{"key":"5_CR4","doi-asserted-by":"publisher","unstructured":"Craythorne, E., Nicholson, P.: Diagnosis and management of skin cancer. Medicine. 51, 2448\u20132452 (2021). https:\/\/doi.org\/10.1016\/j.mpmed.2021.04.007","DOI":"10.1016\/j.mpmed.2021.04.007"},{"key":"5_CR5","doi-asserted-by":"publisher","DOI":"10.1046\/j.1365-2133.2003.05023.x","author":"P Carli","year":"2003","unstructured":"Carli, P., et al.: Pattern analysis, not simplified algorithms, is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology. Br. J. Dermatol. (2003). https:\/\/doi.org\/10.1046\/j.1365-2133.2003.05023.x","journal-title":"Br. J. Dermatol."},{"key":"5_CR6","doi-asserted-by":"publisher","unstructured":"Davenport, T., Kalakota, R.: The potential for artificial intelligence in healthcare. Future Hosp. J. 6, 94\u201398 (2019). https:\/\/doi.org\/10.7861\/futurehosp.6-2-94","DOI":"10.7861\/futurehosp.6-2-94"},{"key":"5_CR7","doi-asserted-by":"publisher","unstructured":"Chollet, F.: Xception: deep learning with Depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800\u20131807. IEEE, USA (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"5_CR8","doi-asserted-by":"publisher","unstructured":"Tan, M., Le,: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019). https:\/\/doi.org\/10.48550\/arxiv.1905.11946","DOI":"10.48550\/arxiv.1905.11946"},{"key":"5_CR9","doi-asserted-by":"publisher","unstructured":"Hu, J., Shen, L., Sun, G., Albanie, S.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(8), 2011\u20132023. (2020). https:\/\/doi.org\/10.1109\/TPAMI.2019.2913372","DOI":"10.1109\/TPAMI.2019.2913372"},{"key":"5_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: Convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"key":"5_CR11","doi-asserted-by":"publisher","unstructured":"Wang, F., et al.: Residual Attention Network for Image Classification (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.683","DOI":"10.1109\/CVPR.2017.683"},{"key":"5_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-3-030-26354-6_10","volume-title":"Advances in Swarm Intelligence","author":"K Boonyuen","year":"2019","unstructured":"Boonyuen, K., Kaewprapha, P., Weesakul, U., Srivihok, P.: Convolutional neural network inception-v3: a machine learning approach for leveling short-range rainfall forecast model from satellite image. In: Tan, Y., Shi, Y., Niu, B. (eds.) ICSI 2019. LNCS, vol. 11656, pp. 105\u2013115. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-26354-6_10"},{"key":"5_CR13","unstructured":"Mnih, V., Heess, N., Graves, A., Kavukcuoglu, K.: Recurrent models of visual attention. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, vol. 2, 2204\u20132212 (2014)"},{"key":"5_CR14","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"5_CR15","doi-asserted-by":"publisher","unstructured":"Dai, J., et al.: Deformable convolutional network. In: IEEE International Conference on Computer Vision (ICCV), pp. 764\u2013773 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.89","DOI":"10.1109\/ICCV.2017.89"},{"key":"5_CR16","unstructured":"Park, J., Woo, S., Lee, J.-Y., Kweon, I.: Bam: Bottleneck attention module (2018)"},{"key":"5_CR17","doi-asserted-by":"publisher","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00813","DOI":"10.1109\/CVPR.2018.00813"},{"key":"5_CR18","doi-asserted-by":"publisher","unstructured":"Huang, Z., Wang, X., Wei, Y., Huang, L., Shi, H., Liu, W.: CCNet: CRISS-cross attention for semantic segmentation. In: Proceedings of the IEEE Transactions on Pattern Analysis and Machine Intelligence, p. 1. (2020). https:\/\/doi.org\/10.1109\/TPAMI.2020.3007032","DOI":"10.1109\/TPAMI.2020.3007032"},{"key":"5_CR19","unstructured":"Geng, Z., Guo, M-H., Chen, H., Li, X., Wei, K., Lin, Z.: Is Attention Better Than Matrix Decomposition? (2021)"},{"key":"5_CR20","doi-asserted-by":"publisher","unstructured":"Liang, S., Gu, Y.: Computer-Aided Diagnosis of Alzheimer\u2019s Disease through Weak Supervision Deep Learning Framework with Attention Mechanism (2020). https:\/\/doi.org\/10.3390\/s21010220","DOI":"10.3390\/s21010220"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR22","doi-asserted-by":"publisher","unstructured":"Jin, Q., Meng, Z., Sun, C., Cui, H., Su, R.: RA-UNet: a hybrid deep attention-aware network to extract liver and tumor in CT scans. Front. Bioeng. Biotechnol. (2020). https:\/\/doi.org\/10.3389\/fbioe.2020.605132","DOI":"10.3389\/fbioe.2020.605132"},{"key":"5_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"5_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-030-87444-5_2","volume-title":"Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data","author":"SK Datta","year":"2021","unstructured":"Datta, S.K., Shaikh, M.A., Srihari, S.N., Gao, M.: Soft attention improves skin cancer classification performance. In: Reyes, M., et al. (eds.) IMIMIC\/TDA4MedicalData -2021. LNCS, vol. 12929, pp. 13\u201323. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87444-5_2"},{"key":"5_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1007\/978-3-030-20351-1_62","volume-title":"Information Processing in Medical Imaging","author":"Y Yan","year":"2019","unstructured":"Yan, Y., Kawahara, J., Hamarneh, G.: Melanoma recognition via visual attention. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 793\u2013804. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20351-1_62"},{"key":"5_CR26","unstructured":"Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition (2014). arXiv:1409.1556"},{"key":"5_CR27","doi-asserted-by":"publisher","unstructured":"Misra, D., Nalamada, T., Arasanipalai, A., Hou, Q.: Rotate to attend: convolutional triplet attention module. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pp. 3138\u20133147 (2020). https:\/\/doi.org\/10.1109\/WACV48630.2021.00318","DOI":"10.1109\/WACV48630.2021.00318"},{"key":"5_CR28","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1038\/s41597-021-00815-z","volume":"8","author":"V Rotemberg","year":"2021","unstructured":"Rotemberg, V., et al.: A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Sci. Data 8, 34 (2021). https:\/\/doi.org\/10.1038\/s41597-021-00815-z","journal-title":"Sci. Data"},{"key":"5_CR29","doi-asserted-by":"publisher","unstructured":"Tschandl P., Rosendahl C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions (2018). https:\/\/doi.org\/10.1038\/sdata.2018.161","DOI":"10.1038\/sdata.2018.161"},{"key":"5_CR30","doi-asserted-by":"publisher","unstructured":"Codella, N.C.F., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI). In: International Skin Imaging Collaboration (ISIC) (2017). https:\/\/doi.org\/10.1109\/ISBI.2018.8363547","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"5_CR31","unstructured":"Combalia, M., et al.: BCN20000: Dermoscopic Lesions in the Wild (2019). arXiv:1908.02288"},{"key":"5_CR32","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a, T., Ferreira, P.M., Marques, J., Marcal, A.R.S., Rozeira, J.: PH$$^2$$ - A dermoscopic image database for research and benchmarking. In: 35th International Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, Japan (2013)","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"5_CR33","unstructured":"DermNet NZ. All about the skin. https:\/\/dermnetnz.org\/image-library. Accessed 26 Jun 2022"},{"key":"5_CR34","doi-asserted-by":"publisher","unstructured":"Kawahara, J., Daneshvar, S., Argenziano, G., Hamarneh, G.: Seven-point checklist and skin lesion classification using multitask multimodal neural nets. In: IEEE Journal of Biomedical Health Informatics (IEEE JBHI) special issue on Skin Lesion Image Analysis for Melanoma Detection (2019). https:\/\/doi.org\/10.1109\/JBHI.2018.2824327","DOI":"10.1109\/JBHI.2018.2824327"},{"key":"5_CR35","unstructured":"Kingma, D., Ba, Jimmy.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015) (2015)"},{"key":"5_CR36","doi-asserted-by":"publisher","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L. C.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520. IEEE, USA (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00474","DOI":"10.1109\/CVPR.2018.00474"},{"key":"5_CR37","doi-asserted-by":"publisher","unstructured":"Indraswari, R., Rokhana, R., Herulambang, W.: Melanoma image classification based on MobileNetV2 network. Proc. Comput. Sci. 197, 198\u2013207 (2022). https:\/\/doi.org\/10.1016\/j.procs.2021.12.132","DOI":"10.1016\/j.procs.2021.12.132"}],"container-title":["Communications in Computer and Information Science","Optimization, Learning Algorithms and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-23236-7_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T02:26:21Z","timestamp":1672539981000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23236-7_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031232350","9783031232367"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23236-7_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"OL2A","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Optimization, Learning Algorithms and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bragan\u00e7a","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ol2a2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ol2a.ipb.pt\/EN_index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"145","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}