{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T21:50:10Z","timestamp":1779227410162,"version":"3.51.4"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030804312","type":"print"},{"value":"9783030804329","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-80432-9_1","type":"book-chapter","created":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T23:08:25Z","timestamp":1625526505000},"page":"3-17","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Exploring the Correlation Between Deep Learned and Clinical Features in Melanoma Detection"],"prefix":"10.1007","author":[{"given":"Tamal","family":"Chowdhury","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Angad R. S.","family":"Bajwa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tapabrata","family":"Chakraborti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jens","family":"Rittscher","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Umapada","family":"Pal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,6]]},"reference":[{"key":"1_CR1","unstructured":"LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks (1995)"},{"key":"1_CR2","unstructured":"Jensen, D., Elewski, B.E.: The ABCDEF rule: combining the ABCDE rule and the ugly duckling sign in an effort to improve patient self-screening examinations. J. Clin. Aesthetic Dermatol. 8(2), 15 (2015)"},{"key":"1_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/978-3-030-02628-8_13","volume-title":"Understanding and Interpreting Machine Learning in Medical Image Computing Applications","author":"P Van Molle","year":"2018","unstructured":"Van Molle, P., De Strooper, M., Verbelen, T., Vankeirsbilck, B., Simoens, P., Dhoedt, B.: Visualizing convolutional neural networks to improve decision support for skin lesion classification. In: Stoyanov, D., et al. (eds.) MLCN\/DLF\/IMIMIC -2018. LNCS, vol. 11038, pp. 115\u2013123. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-02628-8_13"},{"key":"1_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1007\/978-3-030-33850-3_6","volume-title":"Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support","author":"K Young","year":"2019","unstructured":"Young, K., Booth, G., Simpson, B., Dutton, R., Shrapnel, S.: Deep neural network or dermatologist? In: Suzuki, K., et al. (eds.) ML-CDS\/IMIMIC -2019. LNCS, vol. 11797, pp. 48\u201355. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33850-3_6"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"1_CR6","unstructured":"Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, pp. 4765\u20134774 (2017)"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Aggarwal, A., Das, N., Sreedevi, I.: Attention-guided deep convolutional neural networks for skin cancer classification. In: IEEE International Conference on Image Processing Theory, Tools and Applications, pp. 1\u20136 (2019)","DOI":"10.1109\/IPTA.2019.8936100"},{"issue":"9","key":"1_CR8","doi-asserted-by":"publisher","first-page":"2092","DOI":"10.1109\/TMI.2019.2893944","volume":"38","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Xie, Y., Xia, Y., Shen, C.: Attention residual learning for skin lesion classification. IEEE Trans. Med. Imaging 38(9), 2092\u20132103 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 1\u20139 (2018)","DOI":"10.1038\/sdata.2018.161"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Pizer, S.M., et al.: Adaptive histogram equalization and its variations. Comput. Vis. Graphics Image Process. 39(3), 355\u2013368 (1987)","DOI":"10.1016\/S0734-189X(87)80186-X"},{"issue":"1","key":"1_CR11","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62\u201366 (1979)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Zaqout, I.: Diagnosis of skin lesions based on dermoscopic images using image processing techniques. Pattern Recognition-Selected Methods and Applications Intech Open (2019)","DOI":"10.5772\/intechopen.88065"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Amaliah, B., Fatichah, C., Widyanto, M.R.: ABCD feature extraction of image dermatoscopic based on morphology analysis for melanoma skin cancer diagnosis. Jurnal Ilmu Komputer dan Informasi 3(2), 82\u201390 (2010)","DOI":"10.21609\/jiki.v3i2.145"},{"issue":"1","key":"1_CR14","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"issue":"3","key":"1_CR15","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"key":"1_CR16","unstructured":"Bergstra, J., Bardenet, R., Bengio, Y., K\u00e9gl, B.: Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems (2011)"},{"key":"1_CR17","unstructured":"Lin, M., Chen, Q., Yan, S.: Network in network. arXiv:1312.4400 (2013)"},{"key":"1_CR18","unstructured":"Jetley, S., Lord, N.A., Lee, N., Torr, P.H.S.: Learn to pay attention. arXiv:1804.02391 (2018)"},{"key":"1_CR19","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (2017)"},{"key":"1_CR20","unstructured":"Ramachandran, P., Parmar, N., Vaswani, A., Bello, I., Levskaya, A., Shlens, J.: Stand-alone self-attention in vision models. arXiv:1906.05909 (2019)"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"1_CR22","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2014)"},{"key":"1_CR23","unstructured":"Chen, D., Li, J., Xu, K.: AReLU: attention-based rectified linear unit. arXiv:2006.13858 (2020)"},{"key":"1_CR24","unstructured":"Dai, Y., Oehmcke, S., Gieseke, F., Wu, Y., Barnard, K.: Attention as activation. arXiv:2007.07729 (2020)"},{"issue":"11","key":"1_CR25","doi-asserted-by":"publisher","first-page":"3679","DOI":"10.1109\/TMI.2020.3002417","volume":"39","author":"T Eelbode","year":"2020","unstructured":"Eelbode, T., et al.: Optimization for medical image segmentation: theory and practice when evaluating with Dice score or Jaccard index. IEEE Trans. Med. Imaging 39(11), 3679\u20133690 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1_CR26","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.ijmedinf.2019.01.005","volume":"124","author":"N Nida","year":"2019","unstructured":"Nida, N., Irtaza, A., Javed, A., Yousaf, M.H., Mahmood, M.T.: Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. Int. J. Med. Inform. 124, 37\u201348 (2019)","journal-title":"Int. J. Med. Inform."},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Bisla, D., Choromanska, A., Berman, R.S., Stein, J.A., Polsky, D.: Towards automated melanoma detection with deep learning: data purification and augmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00330"},{"key":"1_CR28","first-page":"7160","volume":"8","author":"AA Adekanmi","year":"2019","unstructured":"Adekanmi, A.A., Viriri, S.: Deep learning-based system for automatic melanoma detection. IEEE Access 8, 7160\u20137172 (2019)","journal-title":"IEEE Access"},{"issue":"2","key":"1_CR29","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1007\/s10462-020-09865-y","volume":"54","author":"AA Adekanmi","year":"2021","unstructured":"Adekanmi, A.A., Viriri, S.: Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art. Artif. Intell. Rev. 54(2), 811\u2013841 (2021)","journal-title":"Artif. Intell. Rev."},{"key":"1_CR30","unstructured":"Codella, N., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC). arXiv:1902.03368 (2019)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-80432-9_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:29:48Z","timestamp":1710264588000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-80432-9_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030804312","9783030804329"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-80432-9_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"6 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Oxford","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miua2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miua2021.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"77","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":"32","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":"8","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":"42% - 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":"3,8","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":"3,3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}