{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:10:17Z","timestamp":1742983817638,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811667749"},{"type":"electronic","value":"9789811667756"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-16-6775-6_10","type":"book-chapter","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T20:02:30Z","timestamp":1703016150000},"page":"111-120","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Community Detection in\u00a0Medical Image Datasets: Using Wavelets and\u00a0Spectral Methods"],"prefix":"10.1007","author":[{"given":"Roozbeh","family":"Yousefzadeh","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,20]]},"reference":[{"issue":"1","key":"10_CR1","first-page":"1","volume":"5","author":"HJ Aerts","year":"2014","unstructured":"Aerts, H.J., Velazquez, E.R., Leijenaar, R.T., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et\u00a0al.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications 5(1), 1\u20139 (2014)","journal-title":"Nature Communications"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Angelov, P., Almeida\u00a0Soares, E.: Explainable-by-design approach for COVID-19 classification via CT-scan. medRxiv (2020)","DOI":"10.1101\/2020.04.24.20078584"},{"key":"10_CR3","unstructured":"Birodkar, V., Mobahi, H., Bengio, S.: Semantic redundancies in image-classification datasets: The 10% you don\u2019t need. arXiv preprint arXiv:1901.11409 (2019)"},{"key":"10_CR4","first-page":"67","volume":"88","author":"TF Chan","year":"1987","unstructured":"Chan, T.F.: Rank revealing QR factorizations. Linear Algebra and its Applications 88, 67\u201382 (1987)","journal-title":"Linear Algebra and its Applications"},{"key":"10_CR5","unstructured":"Cohen, J.P., Morrison, P., Dao, L.: Covid-19 image data collection. arXiv 2003.11597 (2020). https:\/\/github.com\/ieee8023\/covid-chestxray-dataset"},{"key":"10_CR6","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.compbiomed.2018.11.001","volume":"104","author":"DK Das","year":"2019","unstructured":"Das, D.K., Dutta, P.K.: Efficient automated detection of mitotic cells from breast histological images using deep convolution neutral network with wavelet decomposed patches. Computers in Biology and Medicine 104, 29\u201342 (2019)","journal-title":"Computers in Biology and Medicine"},{"issue":"5","key":"10_CR7","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1109\/18.57199","volume":"36","author":"I Daubechies","year":"1990","unstructured":"Daubechies, I.: The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory 36(5), 961\u20131005 (1990)","journal-title":"IEEE Transactions on Information Theory"},{"issue":"2","key":"10_CR8","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies, R.J., Kinahan, P.E., Hricak, H.: Radiomics: Images are more than pictures, they are data. Radiology 278(2), 563\u2013577 (2016)","journal-title":"Radiology"},{"key":"10_CR9","first-page":"507","volume":"18","author":"X He","year":"2005","unstructured":"He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. Advances in Neural Information Processing Systems 18, 507\u2013514 (2005)","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"16","key":"10_CR10","first-page":"153","volume":"16","author":"X He","year":"2004","unstructured":"He, X., Niyogi, P.: Locality preserving projections. Advances in Neural Information Processing Systems 16(16), 153\u2013160 (2004)","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10_CR11","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Javed, S., Mahmood, A., Fraz, M.M., Koohbanani, N.A., Benes, K., Tsang, Y.W., Hewitt, K., Epstein, D., Snead, D., Rajpoot, N.: Cellular community detection for tissue phenotyping in colorectal cancer histology images. Medical Image Analysis 63, 101,696 (2020)","DOI":"10.1016\/j.media.2020.101696"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Kather, J.N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.A., Gaiser, T., Marx, A., Valous, N.A., Ferber, D., et\u00a0al.: Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Medicine 16(1), e1002,730 (2019)","DOI":"10.1371\/journal.pmed.1002730"},{"issue":"1","key":"10_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-48023-5","volume":"9","author":"KJ Lafata","year":"2019","unstructured":"Lafata, K.J., Zhou, Z., Liu, J.G., Hong, J., Kelsey, C.R., Yin, F.F.: An exploratory radiomics approach to quantifying pulmonary function in CT images. Scientific Reports 9(1), 1\u20139 (2019)","journal-title":"Scientific Reports"},{"issue":"2","key":"10_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3106370","volume":"12","author":"Y Li","year":"2018","unstructured":"Li, Y., He, K., Kloster, K., Bindel, D., Hopcroft, J.: Local spectral clustering for overlapping community detection. ACM Transactions on Knowledge Discovery from Data (TKDD) 12(2), 1\u201327 (2018)","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"issue":"12","key":"10_CR16","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1049\/iet-ipr.2016.0072","volume":"11","author":"OA Linares","year":"2017","unstructured":"Linares, O.A., Botelho, G.M., Rodrigues, F.A., Neto, J.B.: Segmentation of large images based on super-pixels and community detection in graphs. IET Image Processing 11(12), 1219\u20131228 (2017)","journal-title":"IET Image Processing"},{"issue":"4","key":"10_CR17","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U von Luxburg","year":"2007","unstructured":"von Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395\u2013416 (2007)","journal-title":"Statistics and Computing"},{"issue":"1","key":"10_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41747-018-0068-z","volume":"2","author":"S Rizzo","year":"2018","unstructured":"Rizzo, S., Botta, F., Raimondi, S., Origgi, D., Fanciullo, C., Morganti, A.G., Bellomi, M.: Radiomics: the facts and the challenges of image analysis. European Radiology Experimental 2(1), 1\u20138 (2018)","journal-title":"European Radiology Experimental"},{"key":"10_CR19","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/j.knosys.2018.11.012","volume":"164","author":"P Shi","year":"2019","unstructured":"Shi, P., He, K., Bindel, D., Hopcroft, J.E.: Locally-biased spectral approximation for community detection. Knowledge-Based Systems 164, 459\u2013472 (2019)","journal-title":"Knowledge-Based Systems"},{"issue":"1","key":"10_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-94781-6","volume":"11","author":"E Trivizakis","year":"2021","unstructured":"Trivizakis, E., Ioannidis, G.S., Souglakos, I., Karantanas, A.H., Tzardi, M., Marias, K.: A neural pathomics framework for classifying colorectal cancer histopathology images based on wavelet multi-scale texture analysis. Scientific Reports 11(1), 1\u201310 (2021)","journal-title":"Scientific Reports"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Vo, H.V., Bach, F., Cho, M., Han, K., LeCun, Y., P\u00e9rez, P., Ponce, J.: Unsupervised image matching and object discovery as optimization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8287\u20138296 (2019)","DOI":"10.1109\/CVPR.2019.00848"},{"key":"10_CR22","unstructured":"Yousefzadeh, R.: Using wavelets to analyze similarities in image-classification datasets. arXiv preprint arXiv:2002.10257 (2020)"},{"key":"10_CR23","unstructured":"Yousefzadeh, R., Huang, F.: Using wavelets and spectral methods to study patterns in image-classification datasets. arXiv preprint arXiv:2006.09879 (2020)"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Chen, W., Yang, Y., Wang, Z.: In defense of the triplet loss again: Learning robust person re-identification with fast approximated triplet loss and label distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 354\u2013355 (2020)","DOI":"10.1109\/CVPRW50498.2020.00185"},{"key":"10_CR25","doi-asserted-by":"publisher","first-page":"7834","DOI":"10.1109\/TIP.2020.3006377","volume":"29","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Wei, Y., Wu, Q., Zhao, P., Niu, S., Huang, J., Tan, M.: Collaborative unsupervised domain adaptation for medical image diagnosis. IEEE Transactions on Image Processing 29, 7834\u20137844 (2020)","journal-title":"IEEE Transactions on Image Processing"}],"container-title":["Lecture Notes in Electrical Engineering","Medical Imaging and Computer-Aided Diagnosis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-6775-6_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T18:27:25Z","timestamp":1741112845000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-6775-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789811667749","9789811667756"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-6775-6_10","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"type":"print","value":"1876-1100"},{"type":"electronic","value":"1876-1119"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"20 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Imaging and Computer-Aided Diagnosis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micad2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micad.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}