{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:21:56Z","timestamp":1778689316532,"version":"3.51.4"},"reference-count":56,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T00:00:00Z","timestamp":1707782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education, Youth and Sports of the Czech Republic","award":["LM2018099"],"award-info":[{"award-number":["LM2018099"]}]},{"name":"Ministry of Education, Youth and Sports of the Czech Republic","award":["GAJU 114\/2022\/Z"],"award-info":[{"award-number":["GAJU 114\/2022\/Z"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Multi-class segmentation of unlabelled living cells in time-lapse light microscopy images is challenging due to the temporal behaviour and changes in cell life cycles and the complexity of these images. The deep-learning-based methods achieved promising outcomes and remarkable success in single- and multi-class medical and microscopy image segmentation. The main objective of this study is to develop a hybrid deep-learning-based categorical segmentation and classification method for living HeLa cells in reflected light microscopy images. A symmetric simple U-Net and three asymmetric hybrid convolution neural networks\u2014VGG19-U-Net, Inception-U-Net, and ResNet34-U-Net\u2014were proposed and mutually compared to find the most suitable architecture for multi-class segmentation of our datasets. The inception module in the Inception-U-Net contained kernels with different sizes within the same layer to extract all feature descriptors. The series of residual blocks with the skip connections in each ResNet34-U-Net\u2019s level alleviated the gradient vanishing problem and improved the generalisation ability. The m-IoU scores of multi-class segmentation for our datasets reached 0.7062, 0.7178, 0.7907, and 0.8067 for the simple U-Net, VGG19-U-Net, Inception-U-Net, and ResNet34-U-Net, respectively. For each class and the mean value across all classes, the most accurate multi-class semantic segmentation was achieved using the ResNet34-U-Net architecture (evaluated as the m-IoU and Dice metrics).<\/jats:p>","DOI":"10.3390\/sym16020227","type":"journal-article","created":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T04:18:22Z","timestamp":1707884302000},"page":"227","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Symmetry Breaking in the U-Net: Hybrid Deep-Learning Multi-Class Segmentation of HeLa Cells in Reflected Light Microscopy Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2634-9353","authenticated-orcid":false,"given":"Ali","family":"Ghaznavi","sequence":"first","affiliation":[{"name":"South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Faculty of Fisheries and Protection of Waters, Institute of Complex Systems, University of South Bohemia in \u010cesk\u00e9 Bud\u011bjovice, Z\u00e1mek 136, 37333 Nov\u00e9 Hrady, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3914-3477","authenticated-orcid":false,"given":"Renata","family":"Rycht\u00e1rikov\u00e1","sequence":"additional","affiliation":[{"name":"South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Faculty of Fisheries and Protection of Waters, Institute of Complex Systems, University of South Bohemia in \u010cesk\u00e9 Bud\u011bjovice, Z\u00e1mek 136, 37333 Nov\u00e9 Hrady, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5129-3208","authenticated-orcid":false,"given":"Petr","family":"C\u00edsa\u0159","sequence":"additional","affiliation":[{"name":"South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Faculty of Fisheries and Protection of Waters, Institute of Complex Systems, University of South Bohemia in \u010cesk\u00e9 Bud\u011bjovice, Z\u00e1mek 136, 37333 Nov\u00e9 Hrady, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Mehdi","family":"Ziaei","sequence":"additional","affiliation":[{"name":"South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Faculty of Fisheries and Protection of Waters, Institute of Complex Systems, University of South Bohemia in \u010cesk\u00e9 Bud\u011bjovice, Z\u00e1mek 136, 37333 Nov\u00e9 Hrady, Czech Republic"},{"name":"Faculty of Science, University of South Bohemia, Brani\u0161ovsk\u00e1 1760, 37005 \u010cesk\u00e9 Bud\u011bjovice, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dalibor","family":"\u0160tys","sequence":"additional","affiliation":[{"name":"South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Faculty of Fisheries and Protection of Waters, Institute of Complex Systems, University of South Bohemia in \u010cesk\u00e9 Bud\u011bjovice, Z\u00e1mek 136, 37333 Nov\u00e9 Hrady, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tang, J.R., Mat Isa, N.A., and Ch\u2019ng, E.S. (2015). A Fuzzy-c-Means-Clustering Approach: Quantifying Chromatin Pattern of Non-neoplastic Cervical Squamous Cells. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0142830"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.media.2017.02.009","article-title":"Robust Detection and Segmentation of Cell Nuclei in Biomedical Images Based on a Computational Topology Framework","volume":"38","author":"Xiong","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.patcog.2015.12.009","article-title":"A Semi-Automatic Method for Robust and Efficient Identification of Neighboring Muscle Cells","volume":"53","author":"Wang","year":"2016","journal-title":"Pattern Recogn."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Buggenthin, F., Marr, C., Schwarzfischer, M., Hoppe, P.S., Hilsenbeck, O., Schroeder, T., and Theis, F.J. (2013). An Automatic Method for Robust and Fast Cell Detection in Bright Field Images from High-Throughput Microscopy. BMC Bioinform., 14.","DOI":"10.1186\/1471-2105-14-297"},{"key":"ref_5","unstructured":"Russell, S.J. (2010). Artificial Intelligence: A Modern Approach, Prentice Hall. [3rd ed.]."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Huang, X., Li, C., Shen, M., Shirahama, K., Nyffeler, J., Leist, M., Grzegorzek, M., and Deussen, O. (2016, January 25\u201328). Stem Cell Microscopic Image Segmentation Using Supervised Normalized Cuts. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533139"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mah, S.A., Avci, R., Du, P., Vanderwinden, J.M., and Cheng, L.K. (2020, January 20\u201324). Supervised Machine Learning Segmentation and Quantification of Gastric Pacemaker Cells. Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176445"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tikkanen, T., Ruusuvuori, P., Latonen, L., and Huttunen, H. (2015, January 7\u20139). Training Based Cell Detection from Bright-Field Microscope Images. Proceedings of the 9th International Symposium on Image and Signal Processing and Analysis (ISPA), Zagreb, Croatia.","DOI":"10.1109\/ISPA.2015.7306051"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liimatainen, K., Ruusuvuori, P., Latonen, L., and Huttunen, H. (2016, January 13\u201316). Supervised Method for Cell Counting from Bright Field Focus Stacks. Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic.","DOI":"10.1109\/ISBI.2016.7493290"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hinton, G., and Sejnowski, T. (1999). Unsupervised Learning: Foundations of Neural Computation, MIT Press.","DOI":"10.7551\/mitpress\/7011.001.0001"},{"key":"ref_11","unstructured":"Antal, B., Remenyik, B., and Hajdu, A. (2013, January 29\u201331). An Unsupervised Ensemble-Based Markov Random Field Approach to Microscope Cell Image Segmentation. Proceedings of the 2013 International Conference on Signal Processing and Multimedia Applications (SIGMAP), Reykjavik, Iceland."},{"key":"ref_12","first-page":"377","article-title":"Unsupervised Unstained Cell Detection by SIFT Keypoint Clustering and Self-labeling Algorithm","volume":"Volume 8675","author":"Golland","year":"2014","journal-title":"Medical Image Computing and Computer-Assisted Intervention (MICCAI 2014): Proceedings of the 17th International Conference, Boston, MA, USA, 14\u201318 September 2014"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.micron.2018.01.010","article-title":"Fast and Robust Segmentation of White Blood Cell Images by Self-supervised Learning","volume":"107","author":"Zheng","year":"2018","journal-title":"Micron"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","article-title":"Visualizing and Understanding Convolutional Neural Networks","volume":"Volume 8689","author":"Zeiler","year":"2014","journal-title":"Computer Vision (ECCV 2014): 13th European Conference, Zurich, Switzerland, 6\u201312 September 2014"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lin, S., and Norouzi, N. (2021, January 1\u20135). An Effective Deep Learning Framework for Cell Segmentation in Microscopy Images. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Online.","DOI":"10.1109\/EMBC46164.2021.9629863"},{"key":"ref_16","unstructured":"Kumar, N., Verma, R., Anand, D., and Sethi, A. (2021, May 05). Multi-Organ Nuclei Segmentation Challenge. Available online: https:\/\/monuseg.grandchallenge.org\/."},{"key":"ref_17","unstructured":"Caicedo, J.C., Goodman, A., Karhohs, K.W., Cimini, B.A., Ackerman, J., Haghighi, M., Heng, C., Becker, T., Doan, M., and McQuin, C. (2021, May 05). Broad Bioimage Benchmark Collection. Available online: https:\/\/bbbc.broadinstitute.org\/BBBC038."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Thi Le, P., Pham, T., Hsu, Y.C., and Wang, J.C. (2022). Convolutional Blur Attention Network for Cell Nuclei Segmentation. Sensors, 22.","DOI":"10.3390\/s22041586"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/978-3-319-46976-8_9","article-title":"Fully Convolutional Network for Liver Segmentation and Lesions Detection in Deep Learning and Data Labeling for Medical Applications","volume":"Volume 10008","author":"Carneiro","year":"2016","journal-title":"Proceedings of the Deep Learning and Data Labeling for Medical Applications: 1st International Workshop (LABELS 2016), the 2nd International Workshop (DLMIA 2016), Held in Conjunction with MICCAI 2016"},{"key":"ref_21","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9321","author":"Navab","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, 5\u20139 October 2015"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3791","DOI":"10.1007\/s00371-021-02221-3","article-title":"Cell Image Segmentation by Using Feedback and Convolutional LSTM","volume":"38","author":"Shibuya","year":"2021","journal-title":"Vis. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ghaznavi, A., Rycht\u00e1rikov\u00e1, R., Saberioon, M., and \u0160tys, D. (2022). Cell Segmentation from Telecentric Bright-Field Transmitted Light Microscopy Images Using a Residual Attention U-Net: A Case Study on HeLa line. Comp. Biol. Med., 147.","DOI":"10.1016\/j.compbiomed.2022.105805"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sunny, S.P., Khan, A.I., Rangarajan, M., Hariharan, A., Birur N, P., Pandya, H.J., Shah, N., Kuriakose, M.A., and Suresh, A. (2022). Oral Epithelial Cell Segmentation from Fluorescent Multichannel Cytology Images Using Deep Learning. Comput. Methods Programs Biomed., 227.","DOI":"10.1016\/j.cmpb.2022.107205"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bakir, M.E., and Yalim Keles, v.H. (2021, January 9\u201311). Deep Learning Based Cell Segmentation Using Cascaded U-Net Models. Proceedings of the 2021 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey.","DOI":"10.1109\/SIU53274.2021.9477937"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Piotrowski, T., Rippel, O., Elanzew, A., Nie\u00dfing, B., Stucken, S., Jung, S., K\u00f6nig, N., Haupt, S., Stappert, L., and Br\u00fcstle, O. (2021). Deep-Learning-Based Multi-Class Segmentation for Automated, Non-invasive Routine Assessment of Human Pluripotent Stem Cell Culture Status. Comp. Biol. Med., 129.","DOI":"10.1016\/j.compbiomed.2020.104172"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Platonova, G., \u0160tys, D., Sou\u010dek, P., Lonhus, K., Valenta, J., and Rycht\u00e1rikov\u00e1, R. (2021). Spectroscopic Approach to Correction and Visualization of Bright-Field Light Transmission Microscopy Biological Data. Photonics, 8.","DOI":"10.3390\/photonics8080333"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/978-3-319-31744-1_47","article-title":"Least Information Loss (LIL) Conversion of Digital Images and Lessons Learned for Scientific Image Inspection","volume":"Volume 9656","author":"Ortuno","year":"2016","journal-title":"Bioinformatics and Biomedical Engineering: 4th International Conference (IWBBIO 2016), Granada, Spain, 20\u201322 April 2016"},{"key":"ref_29","unstructured":"Buades, A., Coll, B., and Morel, J.M. (2005, January 20\u201325). A Non-local Algorithm for Image Denoising. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_30","unstructured":"Ghaznavi, A., Rycht\u00e1rikov\u00e1, R., C\u00edsa\u0159, P., Ziaei, M., and \u0160tys, D. (2024, January 01). Telecentric Bright-Field Reflected Light Microscopic Dataset. Available online: https:\/\/doi.org\/10.5061\/dryad.6q573n637."},{"key":"ref_31","unstructured":"(2021, December 12). Zeiss. APPEAR\u2014Automated Image Analysis. Available online: https:\/\/www.apeer.com\/."},{"key":"ref_32","unstructured":"Chen, M. (2021). Computer Vision for Microscopy Image Analysis, Academic Press. Computer Vision and Pattern Recognition."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_34","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 3rd International Conference on Learning Representations (ICLR2015), San Diego, CA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101004","DOI":"10.1016\/j.imu.2022.101004","article-title":"Development and Integration of VGG and Dense Transfer-Learning Systems Supported with Diverse Lung Images for Discovery of the Coronavirus Identity","volume":"32","author":"Hamwi","year":"2022","journal-title":"Inform. Med. Unlocked"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_38","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., and Citro, C. (2016, January 2\u20134). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, Savannah, GA, USA."},{"key":"ref_39","unstructured":"(2021, December 12). Google Research Colaboratory. Available online: https:\/\/colab.research.google.com\/?utm_source=scs-index."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2016.08.103","article-title":"Accurate Segmentation of Nuclei in Pathological Images via Sparse Reconstruction and Deep Convolutional Networks","volume":"229","author":"Pan","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Csurka, G., Larlus, D., and Perronnin, F. (2013, January 9\u201313). What Is a Good Evaluation Measure for Semantic Segmentation?. Proceedings of the British Machine Vision Conference (BMVC 2013), Bristol, UK.","DOI":"10.5244\/C.27.32"},{"key":"ref_43","first-page":"228","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Vijay","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","unstructured":"Sankesara, H. (2024, January 01). UNet. Introducing Symmetry in Segmentation. Available online: https:\/\/towardsdatascience.com\/u-net-b229b32b4a71."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gao, Y., Che, X., Xu, H., and Bie, M. (2023). An enhanced feature extraction network for medical image segmentation. Appl. Sci., 13.","DOI":"10.3390\/app13126977"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Sugimoto, T., Ito, H., Teramoto, Y., Yoshizawa, A., and Bise, R. (2022, January 21\u201324). Multi-Class Cell Detection Using Modified Self-Attention. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00202"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"102182","DOI":"10.1016\/j.media.2021.102182","article-title":"Weakly Supervised Cell Instance Segmentation under Various Conditions","volume":"73","author":"Nishimura","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Long, F. (2020). Microscopy cell nuclei segmentation with enhanced U-Net. BMC Bioinform., 21.","DOI":"10.1186\/s12859-019-3332-1"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.12928\/telkomnika.v18i3.14753","article-title":"UNet-VGG16 with Transfer Learning for MRI-Based Brain Tumor Segmentation","volume":"18","author":"Pravitasari","year":"2020","journal-title":"TELKOMNIKA"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Sharma, N., Saba, L., Khanna, N.N., Kalra, M.K., Fouda, M.M., and Suri, J.S. (2022). Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans. Diagnostics, 12.","DOI":"10.3390\/diagnostics12092132"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.compbiomed.2019.03.014","article-title":"A Novel End-to-End Brain Tumor Segmentation Method Using Improved Fully Convolutional Networks","volume":"108","author":"Li","year":"2019","journal-title":"Comp. Biol. Med."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Patel, G., Tekchandani, H., and Verma, S. (2019, January 20\u201321). Cellular Segmentation of Bright-field Absorbance Images Using Residual U-Net. Proceedings of the 2019 International Conference on Advances in Computing, Communication and Control (ICAC3), Mumbai, India.","DOI":"10.1109\/ICAC347590.2019.9036737"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Gao, E., Jiang, H., Zhou, Z., Yang, C., Chen, M., Zhu, W., Shi, F., Chen, X., Zheng, J., and Bian, Y. (2022). Automatic Multi-Tissue Segmentation in Pancreatic Pathological Images with Selected Multi-Scale Attention Network. Comp. Biol. Med., 151.","DOI":"10.1016\/j.compbiomed.2022.106228"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"101866","DOI":"10.1016\/j.compmedimag.2021.101866","article-title":"Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation","volume":"88","author":"Ho","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"277","DOI":"10.2144\/000113089","article-title":"A novel L1 retrotransposon marker for HeLa cell line identification","volume":"46","author":"Rahbari","year":"2009","journal-title":"Biotechniques"},{"key":"ref_56","unstructured":"Ghaznavi, A. (2024, January 01). GitHub Repository. Available online: https:\/\/github.com\/AliGhaznavi1986\/Hybrid-CNNs-for-multi-class-segmentation."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/2\/227\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:59:12Z","timestamp":1760104752000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/2\/227"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,13]]},"references-count":56,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["sym16020227"],"URL":"https:\/\/doi.org\/10.3390\/sym16020227","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,13]]}}}