{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T04:47:33Z","timestamp":1746679653722,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030687625"},{"type":"electronic","value":"9783030687632"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-68763-2_26","type":"book-chapter","created":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T16:28:24Z","timestamp":1613838504000},"page":"344-356","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Pollen Grain Microscopic Image Classification Using an Ensemble of Fine-Tuned Deep Convolutional Neural Networks"],"prefix":"10.1007","author":[{"given":"Amirreza","family":"Mahbod","sequence":"first","affiliation":[]},{"given":"Gerald","family":"Schaefer","sequence":"additional","affiliation":[]},{"given":"Rupert","family":"Ecker","sequence":"additional","affiliation":[]},{"given":"Isabella","family":"Ellinger","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,21]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Battiato, S., Ortis, A., Trenta, F., Ascari, L., Politi, M., Siniscalco, C.: Detection and classification of pollen grain microscope images. In: Conference on Computer Vision and Pattern Recognition Workshops (2020)","DOI":"10.1109\/CVPRW50498.2020.00498"},{"issue":"7","key":"26_CR2","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1111\/all.13758","volume":"74","author":"T Biedermann","year":"2019","unstructured":"Biedermann, T., Winther, L., Till, S.J., Panzner, P., Knulst, A., Valovirta, E.: Birch pollen allergy in Europe. Allergy 74(7), 1237\u20131248 (2019). https:\/\/doi.org\/10.1111\/all.13758","journal-title":"Allergy"},{"key":"26_CR3","doi-asserted-by":"publisher","unstructured":"Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M.: The balanced accuracy and its posterior distribution. In: International Conference on Pattern Recognition, pp. 3121\u20133124 (2010). https:\/\/doi.org\/10.1109\/ICPR.2010.764","DOI":"10.1109\/ICPR.2010.764"},{"issue":"11","key":"26_CR4","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1002\/jemt.22091","volume":"75","author":"M Chica","year":"2012","unstructured":"Chica, M.: Authentication of bee pollen grains in bright-field microscopy by combining one-class classification techniques and image processing. Microsc. Res. Tech. 75(11), 1475\u20131485 (2012). https:\/\/doi.org\/10.1002\/jemt.22091","journal-title":"Microsc. Res. Tech."},{"key":"26_CR5","unstructured":"Chinchor, N.A., Sundheim, B.: Message understanding conference (MUC) tests of discourse processing. In: Spring Symposium on Empirical Methods in Discourse Interpretation and Generation, pp. 21\u201326 (1995)"},{"key":"26_CR6","doi-asserted-by":"publisher","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995). https:\/\/doi.org\/10.1007\/BF00994018","DOI":"10.1007\/BF00994018"},{"key":"26_CR7","unstructured":"Cruz, A.A.: Global Surveillance, Prevention and Control of Chronic Respiratory Diseases: A Comprehensive Approach. World Health Organization, Geneva (2007)"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Society Conference on Computer Vision and Pattern Recognition. vol. 1 (2005)","DOI":"10.1109\/CVPR.2005.177"},{"key":"26_CR9","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/978-3-319-50835-1_30","volume-title":"Advances in Visual Computing","author":"A Daood","year":"2016","unstructured":"Daood, A., Ribeiro, E., Bush, M.: Pollen grain recognition using deep learning. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Porikli, F., Skaff, S., Entezari, A., Min, J., Iwai, D., Sadagic, A., Scheidegger, C., Isenberg, T. (eds.) Advances in Visual Computing, pp. 321\u2013330. Springer International Publishing, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-50835-1_30"},{"key":"26_CR10","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"9","key":"26_CR11","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1111\/j.1398-9995.2007.01393.x","volume":"62","author":"G D\u2019Amato","year":"2007","unstructured":"D\u2019Amato, G., et al.: Allergenic pollen and pollen allergy in Europe. Allergy 62(9), 976\u2013990 (2007). https:\/\/doi.org\/10.1111\/j.1398-9995.2007.01393.x","journal-title":"Allergy"},{"key":"26_CR12","unstructured":"Fernandez-Delgado, M., Carrion, P., Cernadas, E., Galvez, J., Sa-Otero, P.: Improved classification of pollen texture images using SVM and MLP. In: International Conference on Visualization, Imaging and Image Processing. vol. 2 (2003)"},{"issue":"14\u201315","key":"26_CR13","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","volume":"32","author":"MW Gardner","year":"1998","unstructured":"Gardner, M.W., Dorling, S.: Artificial neural networks (the multilayer perceptron) - a review of applications in the atmospheric sciences. Atmos. Environ. 32(14\u201315), 2627\u20132636 (1998)","journal-title":"Atmos. Environ."},{"key":"26_CR14","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256 (2010)"},{"key":"26_CR15","doi-asserted-by":"publisher","unstructured":"Goncalves, A.B., et al.: Feature extraction and machine learning for the classification of Brazilian Savannah pollen grains. PloS One 11(6), e0157044 (2016). https:\/\/doi.org\/10.1371\/journal.pone.0157044","DOI":"10.1371\/journal.pone.0157044"},{"key":"26_CR16","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-71365-6_1","volume-title":"Illustrated Pollen Terminology","author":"H Halbritter","year":"2018","unstructured":"Halbritter, H., et al.: Palynology: history and systematic aspects. Illustrated Pollen Terminology, pp. 3\u201321. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-71365-6_1"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"26_CR18","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1111\/nph.12848","volume":"203","author":"KA Holt","year":"2014","unstructured":"Holt, K.A., Bennett, K.D.: Principles and methods for automated palynology. N. Phytol. 203(3), 735\u2013742 (2014). https:\/\/doi.org\/10.1111\/nph.12848","journal-title":"N. Phytol."},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"26_CR20","first-page":"4700","volume":"1","author":"G Huang","year":"2017","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. Conf. Comput. Vis. Pattern Recogn. 1, 4700\u20134708 (2017)","journal-title":"Conf. Comput. Vis. Pattern Recogn."},{"key":"26_CR21","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"26_CR22","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"26_CR24","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"key":"26_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1007\/978-3-319-93000-8_85","volume-title":"Image Analysis and Recognition","author":"A Mahbod","year":"2018","unstructured":"Mahbod, A., Ellinger, I., Ecker, R., Smedby, \u00d6., Wang, C.: Breast cancer histological image classification using fine-tuned deep network fusion. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 754\u2013762. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93000-8_85"},{"key":"26_CR26","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.compmedimag.2018.10.007","volume":"71","author":"A Mahbod","year":"2019","unstructured":"Mahbod, A., Schaefer, G., Ellinger, I., Ecker, R., Pitiot, A., Wang, C.: Fusing fine-tuned deep features for skin lesion classification. Computer. Med. Imaging Graph. 71, 19\u201329 (2019). https:\/\/doi.org\/10.1016\/j.compmedimag.2018.10.007","journal-title":"Computer. Med. Imaging Graph."},{"key":"26_CR27","doi-asserted-by":"publisher","unstructured":"Mahbod, A., Schaefer, G., Wang, C., Dorffner, G., Ecker, R., Ellinger, I.: Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification. Comput. Methods Programs Biomed. 193, p. 105475 (2020). https:\/\/doi.org\/10.1016\/j.cmpb.2020.105475","DOI":"10.1016\/j.cmpb.2020.105475"},{"key":"26_CR28","unstructured":"Mahbod, A., Schaefer, G., Wang, C., Ecker, R., Dorffner, G., Ellinger, I.: Investigating and exploiting image resolution for transfer learning-based skin lesion classification. In: 25th International Conference on Pattern Recognition (2020)"},{"key":"26_CR29","doi-asserted-by":"publisher","first-page":"105725","DOI":"10.1016\/j.cmpb.2020.105725","volume":"197","author":"A Mahbod","year":"2020","unstructured":"Mahbod, A., Tschandl, P., Langs, G., Ecker, R., Ellinger, I.: The effects of skin lesion segmentation on the performance of dermatoscopic image classification. Comput. Methods Programs Biomed. 197, 105725 (2020). https:\/\/doi.org\/10.1016\/j.cmpb.2020.105725","journal-title":"Comput. Methods Programs Biomed."},{"key":"26_CR30","unstructured":"Menad, H., Ben-Naoum, F., Amine, A.: Deep convolutional neural network for pollen grains classification. In: 3rd Edition of the National Study Day on Research on Computer Sciences. CEUR Workshop Proceedings, vol. 2351 (2019)"},{"key":"26_CR31","unstructured":"Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971\u2013987 (2002)"},{"issue":"1","key":"26_CR32","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.patrec.2006.06.010","volume":"28","author":"M Ranzato","year":"2007","unstructured":"Ranzato, M., Taylor, P., House, J., Flagan, R., LeCun, Y., Perona, P.: Automatic recognition of biological particles in microscopic images. Pattern Recogn. Lett. 28(1), 31\u201339 (2007)","journal-title":"Pattern Recogn. Lett."},{"issue":"9","key":"26_CR33","doi-asserted-by":"publisher","first-page":"e0201807","DOI":"10.1371\/journal.pone.0201807","volume":"13","author":"V Sevillano","year":"2018","unstructured":"Sevillano, V., Aznarte, J.L.: Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks. PloS One 13(9), e0201807 (2018). https:\/\/doi.org\/10.1371\/journal.pone.0201807","journal-title":"PloS One"},{"issue":"6","key":"26_CR34","doi-asserted-by":"publisher","first-page":"e0229751","DOI":"10.1371\/journal.pone.0229751","volume":"15","author":"V Sevillano","year":"2020","unstructured":"Sevillano, V., Holt, K., Aznarte, J.L.: Precise automatic classification of 46 different pollen types with convolutional neural networks. PLoS One 15(6), e0229751 (2020). https:\/\/doi.org\/10.1371\/journal.pone.0229751","journal-title":"PLoS One"},{"key":"26_CR35","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"issue":"1","key":"26_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0277-3791(95)00076-3","volume":"15","author":"E Stillman","year":"1996","unstructured":"Stillman, E., Flenley, J.R.: The needs and prospects for automation in palynology. Quat. Sci. Rev. 15(1), 1\u20135 (1996)","journal-title":"Quat. Sci. Rev."},{"key":"26_CR37","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"26_CR38","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"26_CR39","unstructured":"Tan, M., Le, Q.V.: EfficientNet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946 (2019)"},{"key":"26_CR40","doi-asserted-by":"publisher","unstructured":"Travieso, C.M., Brice\u00f1o, J.C., Ticay-Rivas, J.R., Alonso, J.B.: Pollen classification based on contour features. In: International Conference on Intelligent Engineering Systems (2011). https:\/\/doi.org\/10.1109\/INES.2011.5954712","DOI":"10.1109\/INES.2011.5954712"},{"key":"26_CR41","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Conference on Computer Vision and Pattern Recognition, pp. 5987\u20135995 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"26_CR42","doi-asserted-by":"publisher","unstructured":"Zhang, Z.: Deep-learning-based early detection of diabetic retinopathy on fundus photography using efficientnet. In: International Conference on Innovation in Artificial Intelligence, pp. 70\u201374 (2020). https:\/\/doi.org\/10.1145\/3390557.3394303","DOI":"10.1145\/3390557.3394303"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition. ICPR International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68763-2_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T15:11:47Z","timestamp":1724512307000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68763-2_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030687625","9783030687632"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68763-2_26","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":"21 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ICPR2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icpr2020.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}