{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:33:55Z","timestamp":1742945635884,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030845285"},{"type":"electronic","value":"9783030845292"}],"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\/978-3-030-84529-2_49","type":"book-chapter","created":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T15:01:42Z","timestamp":1628521302000},"page":"579-591","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-Channel Recalibration and Feature Fusion Method for Liver Image Classification"],"prefix":"10.1007","author":[{"given":"Tingting","family":"Niu","sequence":"first","affiliation":[]},{"given":"Xiaolong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chunhua","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Ruoqin","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,9]]},"reference":[{"key":"49_CR1","doi-asserted-by":"crossref","unstructured":"Hessinger N\u00e9e Reimann, C.: Dielectric contrast between normal and tumor ex-vivo human liver tissue. IEEE Access 164113\u2013164119(2019)","DOI":"10.1109\/ACCESS.2019.2951617"},{"key":"49_CR2","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.media.2018.10.010","volume":"51","author":"N Khosravan","year":"2019","unstructured":"Khosravan, N., Celik, H., Turkbey, B.: A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning. Med. Image Anal. 51, 101\u2013115 (2019)","journal-title":"Med. Image Anal."},{"issue":"7","key":"49_CR3","doi-asserted-by":"publisher","first-page":"3071","DOI":"10.1109\/TIP.2014.2325777","volume":"23","author":"L Liu","year":"2014","unstructured":"Liu, L., Long, Y., Fieguth, P.W.: BRINT: binary rotation invariant and noise tolerant texture classification. IEEE Trans Image Process 23(7), 3071\u20133084 (2014)","journal-title":"IEEE Trans Image Process"},{"key":"49_CR4","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1007\/978-81-322-2126-5_80","volume-title":"Artificial Intelligence and Evolutionary Algorithms in Engineering Systems","author":"S Ramamoorthy","year":"2015","unstructured":"Ramamoorthy, S., Kirubakaran, R., Subramanian, R.S.: Texture feature extraction using mgrlbp method for medical image classification. In: Suresh, L.P., Dash, S.S., Panigrahi, B.K. (eds.) Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. AISC, vol. 324, pp. 747\u2013753. Springer, New Delhi (2015). https:\/\/doi.org\/10.1007\/978-81-322-2126-5_80"},{"key":"49_CR5","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1109\/TNNLS.2019.2899744","volume":"31","author":"D Bacciu","year":"2020","unstructured":"Bacciu, D., Crecchi, F.: Augmenting recurrent neural networks resilience by dropout. IEEE Trans. Neural Netw. Learn. Syst. 31, 345\u2013351 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"49_CR6","doi-asserted-by":"crossref","unstructured":"Zhu, F., Zhu, B., Li, P., Wang, Z., Wei, L.: Quantitative analysis and identification of liver B-scan ultrasonic image based on BP neural network. In: 2013 International Conference on Optoelectronics and Microelectronics (ICOM), pp. 62\u201366 (2013)","DOI":"10.1109\/ICoOM.2013.6626491"},{"key":"49_CR7","doi-asserted-by":"crossref","unstructured":"Romero, F.P.: End-to-end discriminative deep network for liver lesion classification. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1243\u20131246(2019)","DOI":"10.1109\/ISBI.2019.8759257"},{"key":"49_CR8","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S.: Rethinking the Inception Architecture for Computer Vision. 32, 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"49_CR9","doi-asserted-by":"publisher","first-page":"2362","DOI":"10.1109\/TVCG.2018.2835485","volume":"25","author":"X Xie","year":"2019","unstructured":"Xie, X., Cai, X., Zhou, J., Cao, N.: A semantic-based method for visualizing large image collections. IEEE Trans. Vis. Comput. Graph. 25, 2362\u20132377 (2019)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"49_CR10","doi-asserted-by":"crossref","unstructured":"Chen, S., Sun, W., Huang, L.: Compressing fully connected layers using Kronecker tensor decomposition. In: 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), pp. 308\u2013312 (2019)","DOI":"10.1109\/ICCSNT47585.2019.8962432"},{"key":"49_CR11","doi-asserted-by":"publisher","first-page":"146754","DOI":"10.1109\/ACCESS.2020.3015312","volume":"8","author":"J Zheng","year":"2020","unstructured":"Zheng, J., Wu, Y., Song, W.: Multi-scale feature channel attention generative adversarial network for face sketch synthesis. IEEE Access 8, 146754\u2013146769 (2020)","journal-title":"IEEE Access"},{"key":"49_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, D.: clcNet: improving the efficiency of convolutional neural network using channel local convolutions. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7912\u20137919 (2018)","DOI":"10.1109\/CVPR.2018.00825"},{"key":"49_CR13","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"49_CR14","doi-asserted-by":"crossref","unstructured":"Hara, K., Kataoka, H., Satoh, Y.: Can spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6546\u20136555 (2018)","DOI":"10.1109\/CVPR.2018.00685"},{"key":"49_CR15","doi-asserted-by":"crossref","unstructured":"Chiu, M.T.: Agriculture-vision: a large aerial image database for agricultural pattern analysis. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2825\u20132835(2020)","DOI":"10.1109\/CVPR42600.2020.00290"},{"key":"49_CR16","doi-asserted-by":"publisher","first-page":"18940","DOI":"10.1109\/ACCESS.2019.2895688","volume":"7","author":"SU Amin","year":"2019","unstructured":"Amin, S.U., Alsulaiman, M., Muhammad, G.A.M.: Multilevel weighted feature fusion using convolutional neural networks for EEG motor imagery classification. IEEE Access 7, 18940\u201318950 (2019)","journal-title":"IEEE Access"},{"key":"49_CR17","doi-asserted-by":"crossref","unstructured":"Gong, H., Zhang, H., Zhou, L.: An interpretable artificial intelligence model of chinese medicine treatment based on XGBoost algorithm. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1550\u20131554 (2020)","DOI":"10.1109\/BIBM49941.2020.9313424"},{"key":"49_CR18","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1109\/TNNLS.2017.2649101","volume":"29","author":"K Gu","year":"2018","unstructured":"Gu, K., Tao, D., Qiao, J.: Learning a no-reference quality assessment model of enhanced images with big data. IEEE Trans. Neural Netw. Learn. Syst. 29, 1301\u20131313 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"49_CR19","doi-asserted-by":"crossref","unstructured":"Udhayakumar, R.K., Karmakar, C., Palaniswami, M.: Cross entropy profiling to test pattern synchrony in short-term signals. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 737\u2013740 (2019)","DOI":"10.1109\/EMBC.2019.8857272"},{"key":"49_CR20","doi-asserted-by":"crossref","unstructured":"Xia, Y., Cai, M., Ni, C.: A switch state recognition method based on improved VGG19 network. In: 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 1658\u20131662 (2019)","DOI":"10.1109\/IAEAC47372.2019.8998029"}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-84529-2_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T03:38:25Z","timestamp":1725593905000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-84529-2_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030845285","9783030845292"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-84529-2_49","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2021a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2021\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}