{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T16:12:47Z","timestamp":1725811967997},"reference-count":73,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871274","61801305","81571758","61872351"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012326","name":"International Science and Technology Cooperation Projects of Guangdong, China","doi-asserted-by":"publisher","award":["2019A050510030"],"id":[{"id":"10.13039\/501100012326","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012234","name":"Shenzhen Peacock Plan","doi-asserted-by":"publisher","award":["KQTD2016053112051497","KQTD2015033016104926"],"id":[{"id":"10.13039\/501100012234","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Key Basic Research Project","award":["JCYJ20180507184647636","JCYJ20170818094109846"]},{"DOI":"10.13039\/501100009019","name":"SZU Medical Young Scientists Program, China","doi-asserted-by":"publisher","award":["71201-000001"],"id":[{"id":"10.13039\/501100009019","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1016\/j.asoc.2020.106281","type":"journal-article","created":{"date-parts":[[2020,4,7]],"date-time":"2020-04-07T21:36:07Z","timestamp":1586295367000},"page":"106281","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":40,"special_numbering":"C","title":["Convolutional descriptors aggregation via cross-net for skin lesion recognition"],"prefix":"10.1016","volume":"92","author":[{"given":"Zhen","family":"Yu","sequence":"first","affiliation":[]},{"given":"Feng","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Xinzi","family":"He","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Siping","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Tianfu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Baiying","family":"Lei","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2020.106281_b1","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1049\/iet-ipr.2015.0385","article-title":"Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule","volume":"10","author":"Kasmi","year":"2016","journal-title":"IET Image Process."},{"key":"10.1016\/j.asoc.2020.106281_b2","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.compmedimag.2007.01.003","article-title":"A methodological approach to the classification of dermoscopy images","volume":"31","author":"Celebi","year":"2007","journal-title":"Comput. Med. Imag. Grap."},{"key":"10.1016\/j.asoc.2020.106281_b3","article-title":"A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data","volume":"8318","author":"Ali","year":"2012","journal-title":"SPIE Med. Imag."},{"key":"10.1016\/j.asoc.2020.106281_b4","doi-asserted-by":"crossref","unstructured":"N. Codella, J. Cai, M. Abedini, R. Garnavi, A. Halpern, J.R. Smith, Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images, in: Proc. Med. Imag. Comput. Comput. Assist. Interv. 2015, pp. 118\u2013126.","DOI":"10.1007\/978-3-319-24888-2_15"},{"year":"2016","series-title":"Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC)","author":"Gutman","key":"10.1016\/j.asoc.2020.106281_b5"},{"key":"10.1016\/j.asoc.2020.106281_b6","first-page":"521","article-title":"ABCD Rule of dermatoscopy : A new practical method for early recognition of malignant melanoma","volume":"4","author":"Stolz","year":"1994","journal-title":"Eur. J. Dermatol."},{"key":"10.1016\/j.asoc.2020.106281_b7","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1001\/archderm.1996.03890340038007","article-title":"Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features","volume":"132","author":"Menzies","year":"1996","journal-title":"Arch. Dermatol."},{"key":"10.1016\/j.asoc.2020.106281_b8","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1001\/archderm.134.12.1563","article-title":"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","volume":"134","author":"Argenziano","year":"1998","journal-title":"Arch. Dermatol."},{"key":"10.1016\/j.asoc.2020.106281_b9","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.jaad.2006.09.003","article-title":"The CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy","volume":"56","author":"Henning","year":"2007","journal-title":"J. Amer. Acad. Dermatol."},{"key":"10.1016\/j.asoc.2020.106281_b10","doi-asserted-by":"crossref","unstructured":"U. Jamil, S. Khalid, Comparative study of classification techniques used in skin lesion detection systems, in: IEEE Int. Multitopic Conf. 2014, pp. 266\u2013271.","DOI":"10.1109\/INMIC.2014.7097349"},{"key":"10.1016\/j.asoc.2020.106281_b11","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1007\/s00521-016-2482-6","article-title":"Computational methods for pigmented skin lesion classification in images: review and future trends","volume":"29","author":"Oliveira","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.asoc.2020.106281_b12","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.artmed.2012.08.002","article-title":"Computerized analysis of pigmented skin lesions: A review","volume":"56","author":"Korotkov","year":"2012","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.asoc.2020.106281_b13","doi-asserted-by":"crossref","unstructured":"L. Bi, J. Kim, E. Ahn, D. Feng, M. Fulham, Automatic melanoma detection via multi-scale lesion-biased representation and joint reverse classification, in: Proc. IEEE 13th Int. Symp. Biomed. Imag, 2016, pp. 1055\u20131058.","DOI":"10.1109\/ISBI.2016.7493447"},{"key":"10.1016\/j.asoc.2020.106281_b14","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/S0010-4825(97)00020-6","article-title":"Dullrazor\u00ae: A software approach to hair removal from images","volume":"27","author":"Lee","year":"1997","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2020.106281_b15","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TMI.2014.2305769","article-title":"Model-based classification methods of global patterns in dermoscopic images","volume":"33","author":"S\u00e1ez","year":"2014","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.asoc.2020.106281_b16","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.compmedimag.2010.08.003","article-title":"Automated color calibration method for dermoscopy images","volume":"35","author":"Iyatomi","year":"2011","journal-title":"Comput. Med. Imag. Graph."},{"key":"10.1016\/j.asoc.2020.106281_b17","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compmedimag.2010.08.004","article-title":"Colour and contrast enhancement for improved skin lesion segmentation","volume":"35","author":"Schaefer","year":"2011","journal-title":"Comput. Med. Imag. Graph."},{"key":"10.1016\/j.asoc.2020.106281_b18","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1109\/42.918473","article-title":"Automated melanoma recognition","volume":"20","author":"Ganster","year":"2001","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.asoc.2020.106281_b19","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.compmedimag.2015.02.011","article-title":"Automatic differentiation of melanoma from dysplastic nevi","volume":"43","author":"Rastgoo","year":"2015","journal-title":"Comput. Med. Imag. Graph."},{"key":"10.1016\/j.asoc.2020.106281_b20","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1109\/TBME.2014.2348323","article-title":"Four-class classification of skin lesions with task decomposition strategy","volume":"62","author":"Shimizu","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.asoc.2020.106281_b21","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1016\/j.patrec.2011.06.015","article-title":"Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions","volume":"32","author":"Capdehourat","year":"2011","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.asoc.2020.106281_b22","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1109\/TMI.2016.2633551","article-title":"Melanoma classification on dermoscopy images using a neural network ensemble model","volume":"36","author":"Xie","year":"2017","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.asoc.2020.106281_b23","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1109\/TMI.2016.2606380","article-title":"Accurate cervical cell segmentation from overlapping clumps in pap smear images","volume":"36","author":"Song","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2020.106281_b24","doi-asserted-by":"crossref","first-page":"2421","DOI":"10.1109\/TBME.2015.2430895","article-title":"Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning","volume":"62","author":"Song","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.asoc.2020.106281_b25","unstructured":"S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2006, pp. 2169\u20132178."},{"key":"10.1016\/j.asoc.2020.106281_b26","unstructured":"G. Csurka, C. Dance, L. Fan, J. Willamowski, C. Bray, Visual categorization with bags of keypoints, in: Proc. 8th Eur. Conf. Comput. Vis.Workshop Statistical Learn. Comput. Vis. 2004, pp. 950\u2013953."},{"key":"10.1016\/j.asoc.2020.106281_b27","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1109\/TPAMI.2011.235","article-title":"Aggregating local image descriptors into compact codes","volume":"34","author":"J\u00e9gou","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2020.106281_b28","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1007\/s11263-013-0636-x","article-title":"Image classification with the fisher vector: Theory and practice","volume":"105","author":"S\u00e1nchez","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.asoc.2020.106281_b29","doi-asserted-by":"crossref","unstructured":"K. Chatfield, V.S. Lempitsky, A. Vedaldi, A. Zisserman, The devil is in the details: An evaluation of recent feature encoding methods, in: Proc. Brit. Mach. Vis. Conf. 2011, pp. 76.71-76.12.","DOI":"10.5244\/C.25.76"},{"key":"10.1016\/j.asoc.2020.106281_b30","doi-asserted-by":"crossref","unstructured":"F. Perronnin, J. S\u00e1nchez, T. Mensink, Improving the fisher kernel for large-scale image classification, in: Proc. 11th Eur. Conf. Comput. Vis. 2010, pp. 143\u2013156.","DOI":"10.1007\/978-3-642-15561-1_11"},{"key":"10.1016\/j.asoc.2020.106281_b31","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"year":"2015","series-title":"Deep residual learning for image recognition","author":"He","key":"10.1016\/j.asoc.2020.106281_b32"},{"key":"10.1016\/j.asoc.2020.106281_b33","doi-asserted-by":"crossref","unstructured":"J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2015, pp. 3431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"10.1016\/j.asoc.2020.106281_b34","doi-asserted-by":"crossref","unstructured":"C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, A. Rabinovich, Going deeper with convolutions, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2015, pp. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10.1016\/j.asoc.2020.106281_b35","unstructured":"J. Yosinski, J. Clune, Y. Bengio, H. Lipson, How transferable are features in deep neural networks?, in: Proc. Adv. Neural Inform. Process. Syst. 2014, pp. 3320\u20133328."},{"key":"10.1016\/j.asoc.2020.106281_b36","unstructured":"A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: Proc. Adv. Neural Inform. Process. Syst. 2012, pp. 1097\u20131105."},{"year":"2014","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","key":"10.1016\/j.asoc.2020.106281_b37"},{"key":"10.1016\/j.asoc.2020.106281_b38","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"year":"2016","series-title":"Good practice in CNN feature transfer","author":"Zheng","key":"10.1016\/j.asoc.2020.106281_b39"},{"key":"10.1016\/j.asoc.2020.106281_b40","doi-asserted-by":"crossref","unstructured":"Y. Gong, L. Wang, R. Guo, S. Lazebnik, Multi-scale orderless pooling of deep convolutional activation features, in: Proc. 13th Eur. Conf. Comput. Vis. Proc. 13th Eur. Conf. Comput. Vis. 2014, pp. 392\u2013407.","DOI":"10.1007\/978-3-319-10584-0_26"},{"key":"10.1016\/j.asoc.2020.106281_b41","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/TMI.2016.2642839","article-title":"Automated melanoma recognition in dermoscopy images via very deep residual networks","volume":"36","author":"Yu","year":"2017","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.asoc.2020.106281_b42","doi-asserted-by":"crossref","unstructured":"J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, Imagenet: A large-scale hierarchical image database, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248\u2013255.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"10.1016\/j.asoc.2020.106281_b43","doi-asserted-by":"crossref","unstructured":"M. Cimpoi, S. Maji, A. Vedaldi, Deep filter banks for texture recognition and segmentation, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2015, pp. 3828\u20133836.","DOI":"10.1109\/CVPR.2015.7299007"},{"key":"10.1016\/j.asoc.2020.106281_b44","unstructured":"J.Y. Ng, F. Yang, L.S. Davis, Exploiting local features from deep networks for image retrieval, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshop, 2015, pp. 53\u201361."},{"key":"10.1016\/j.asoc.2020.106281_b45","doi-asserted-by":"crossref","unstructured":"Y. Gong, L. Wang, R. Guo, S. Lazebnik, Multi-scale orderless pooling of deep convolutional activation features, in, Proc. 13th Eur. Conf. Comput. Vis. 2014, pp. 392\u2013407.","DOI":"10.1007\/978-3-319-10584-0_26"},{"key":"10.1016\/j.asoc.2020.106281_b46","doi-asserted-by":"crossref","unstructured":"D. Yoo, S. Park, J. Lee, K. In\u00a0So, Multi-scale pyramid pooling for deep convolutional representation, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, 2015, pp. 71\u201380.","DOI":"10.1109\/CVPRW.2015.7301274"},{"key":"10.1016\/j.asoc.2020.106281_b47","doi-asserted-by":"crossref","unstructured":"Z. Yu, X. Jiang, T. Wang, B. Lei, Aggregating deep convolutional features for melanoma recognition in dermoscopy images, in: Proc. Mach. Learn. Med. Imag. 2017, pp. 238\u2013246.","DOI":"10.1007\/978-3-319-67389-9_28"},{"key":"10.1016\/j.asoc.2020.106281_b48","doi-asserted-by":"crossref","unstructured":"T. Lin, A. RoyChowdhury, S. Maji, Bilinear CNN models for fine-grained visual recognition, in: Proc. IEEE Int. Conf. Comput. Vis. 2015, pp. 1449\u20131457.","DOI":"10.1109\/ICCV.2015.170"},{"key":"10.1016\/j.asoc.2020.106281_b49","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1109\/TPAMI.2016.2637921","article-title":"Cross-convolutional-layer pooling for image recognition","volume":"39","author":"Liu","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2020.106281_b50","doi-asserted-by":"crossref","first-page":"2868","DOI":"10.1109\/TIP.2017.2688133","article-title":"Selective convolutional descriptor aggregation for fine-grained image retrieval","volume":"26","author":"Wei","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.asoc.2020.106281_b51","doi-asserted-by":"crossref","unstructured":"N. Situ, X. Yuan, J. Chen, G. Zouridakis, Malignant melanoma detection by Bag-of-Features classification, in: Proc. IEEE Eng. Med. Bio. Society, 2008, pp. 3110\u20133113.","DOI":"10.1109\/IEMBS.2008.4649862"},{"key":"10.1016\/j.asoc.2020.106281_b52","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.asoc.2020.106281_b53","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/JSYST.2013.2271540","article-title":"Two systems for the detection of melanomas in dermoscopy images using texture and color features","volume":"8","author":"Barata","year":"2014","journal-title":"IEEE Syst. J."},{"key":"10.1016\/j.asoc.2020.106281_b54","doi-asserted-by":"crossref","unstructured":"J. Kawahara, A. BenTaieb, G. Hamarneh, Deep features to classify skin lesions, in: Proc. IEEE 13th Int. Symp. Biomed. Imag. 2016, pp. 1397\u20131400.","DOI":"10.1109\/ISBI.2016.7493528"},{"year":"2016","series-title":"Deep learning ensembles for melanoma recognition in dermoscopy images","author":"Codella","key":"10.1016\/j.asoc.2020.106281_b55"},{"key":"10.1016\/j.asoc.2020.106281_b56","doi-asserted-by":"crossref","unstructured":"O. Ronneberger, P. Fischer, T. Brox, U-Net: Convolutional networks for biomedical image segmentation, in: Proc. Med. Imag. Comput. Comput. Assist. Interv. 2015, pp. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10.1016\/j.asoc.2020.106281_b57","doi-asserted-by":"crossref","unstructured":"Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell, Caffe: Convolutional architecture for fast feature embedding, in: Proc. 22nd ACM Int. Conf. Multi. 2014, pp. 675\u2013678.","DOI":"10.1145\/2647868.2654889"},{"key":"10.1016\/j.asoc.2020.106281_b58","doi-asserted-by":"crossref","unstructured":"S. Demyanov, R. Chakravorty, M. Abedini, A. Halpern, R. Garnavi, Classification of dermoscopy patterns using deep convolutional neural networks, in: Proc. IEEE 13th Int. Symp. Biomed. Imag. 2016, pp. 364\u2013368.","DOI":"10.1109\/ISBI.2016.7493284"},{"key":"10.1016\/j.asoc.2020.106281_b59","first-page":"1146","article-title":"Improving dermoscopy image classification using color constancy","volume":"19","author":"Barata","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"year":"2017","series-title":"Data-driven color augmentation techniques for deep skin image analysis","author":"Galdran","key":"10.1016\/j.asoc.2020.106281_b60"},{"key":"10.1016\/j.asoc.2020.106281_b61","doi-asserted-by":"crossref","unstructured":"G.D. Finlayson, E. Trezzi, Shades of gray and colour constancy, in: Color & Imaging Conference, 2004.","DOI":"10.2352\/CIC.2004.12.1.art00008"},{"key":"10.1016\/j.asoc.2020.106281_b62","unstructured":"J. Long, N. Zhang, T. Darrell, Do convnets learn correspondence?, in: Proc. Adv. Neural Inform. Process. Syst. 2014, pp. 1601\u20131609."},{"key":"10.1016\/j.asoc.2020.106281_b63","unstructured":"C.S.L. Liu, L. Wang, A. Hengel, C. Wang, Encoding high dimensional local features by sparse coding based fisher vectors, in: Proc. Adv. Neural Inform. Process. Syst. 2014, pp. 1143\u20131151."},{"key":"10.1016\/j.asoc.2020.106281_b64","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1109\/TIP.2016.2629443","article-title":"Locally supervised deep hybrid model for scene recognition","volume":"26","author":"Guo","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.asoc.2020.106281_b65","doi-asserted-by":"crossref","first-page":"2348","DOI":"10.1016\/j.patcog.2013.10.011","article-title":"Fisher Tensors for classifying human epithelial cells","volume":"47","author":"Faraki","year":"2014","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2020.106281_b66","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1016\/j.patcog.2016.09.037","article-title":"Multi-modal and multi-layout discriminative learning for placental maturity staging","volume":"63","author":"Lei","year":"2017","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2020.106281_b67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc."},{"key":"10.1016\/j.asoc.2020.106281_b68","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"10.1016\/j.asoc.2020.106281_b69","first-page":"567","article-title":"Stochastic dual coordinate ascent methods for regularized loss minimization","volume":"14","author":"Shalev-Shwartz","year":"2013","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.asoc.2020.106281_b70","doi-asserted-by":"crossref","unstructured":"A. Vedaldi, K. Lenc, MatConvNet: Convolutional neural networks for MATLAB, in: Proc. 23th Int. Conf. Multimed. 2015, pp. 689\u2013692.","DOI":"10.1145\/2733373.2807412"},{"key":"10.1016\/j.asoc.2020.106281_b71","doi-asserted-by":"crossref","unstructured":"A. Vedaldi, B. Fulkerson, Vlfeat: an open and portable library of computer vision algorithms, in: Proc. Int. Conf. Multimed. 2010, pp. 1469\u20131472.","DOI":"10.1145\/1873951.1874249"},{"key":"10.1016\/j.asoc.2020.106281_b72","doi-asserted-by":"crossref","unstructured":"A.S. Razavian, H. Azizpour, J. Sullivan, S. Carlsson, CNN features off-the-shelf: An astounding baseline for recognition, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. DeepVision Workshop, 2014, pp. 806\u2013813.","DOI":"10.1109\/CVPRW.2014.131"},{"key":"10.1016\/j.asoc.2020.106281_b73","doi-asserted-by":"crossref","unstructured":"T. Uricchio, M. Bertini, L. Seidenari, A.D. Bimbo, Fisher encoded convolutional bag-of-windows for efficient image retrieval and social image tagging, in: Proc. IEEE Inter. Conf. Comput. Vision Workshop, 2015, pp. 1020\u20131026.","DOI":"10.1109\/ICCVW.2015.134"}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494620302210?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494620302210?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T16:24:20Z","timestamp":1722702260000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494620302210"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":73,"alternative-id":["S1568494620302210"],"URL":"http:\/\/dx.doi.org\/10.1016\/j.asoc.2020.106281","relation":{},"ISSN":["1568-4946"],"issn-type":[{"type":"print","value":"1568-4946"}],"subject":[],"published":{"date-parts":[[2020,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Convolutional descriptors aggregation via cross-net for skin lesion recognition","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2020.106281","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"106281"}}
