{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T06:45:57Z","timestamp":1785653157211,"version":"3.56.0"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032316653","type":"print"},{"value":"9783032316660","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-31666-0_41","type":"book-chapter","created":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T05:47:09Z","timestamp":1785649629000},"page":"629-644","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Label-Free Single-Cell Phenotyping Using Multi-task Learning"],"prefix":"10.1007","author":[{"given":"Saqib","family":"Nazir","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ardhendu","family":"Behera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"41_CR1","unstructured":"Chen, J.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"issue":"5","key":"41_CR2","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1039\/D3LC00385J","volume":"24","author":"G Ciaparrone","year":"2024","unstructured":"Ciaparrone, G., et al.: Label-free cell classification in holographic flow cytometry through an unbiased learning strategy. Lab Chip 24(5), 924\u2013932 (2024)","journal-title":"Lab Chip"},{"key":"41_CR3","unstructured":"Dan, H., et\u00a0al.: Gaussian error linear units (gelus). arXiv: Learning (2016). https:\/\/api.semanticscholar.org\/CorpusID:125617073"},{"key":"41_CR4","unstructured":"Dosovitskiy, A.: An Image is Worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021)"},{"key":"41_CR5","unstructured":"Google DeepMind: Gemini 2.5 pro model card (2024). https:\/\/deepmind.google\/"},{"key":"41_CR6","volume":"111","author":"M Habibzadeh","year":"2021","unstructured":"Habibzadeh, M., et al.: A review on automatic analysis of blood cells: from image acquisition to classification. Artif. Intell. Med. 111, 102005 (2021)","journal-title":"Artif. Intell. Med."},{"key":"41_CR7","doi-asserted-by":"crossref","unstructured":"Kobayashi-Kirschvink, K.J., et\u00a0al.: Raman2rna: Live-cell label-free prediction of single-cell rna expression profiles by raman microscopy. bioRxiv pp. 2021\u201311 (2021)","DOI":"10.1101\/2021.11.30.470655"},{"issue":"1","key":"41_CR8","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1038\/s41598-021-04426-x","volume":"12","author":"Z Kouzehkanan","year":"2022","unstructured":"Kouzehkanan, Z., et al.: A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm. Sci. Rep. 12(1), 1123 (2022)","journal-title":"Sci. Rep."},{"key":"41_CR9","unstructured":"Li, Y.: Clinical-t5: a text-to-text transformer for clinical language understanding. J. Biomed. Inform. (2023)"},{"key":"41_CR10","doi-asserted-by":"crossref","unstructured":"Lin, T.Y.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"41_CR11","doi-asserted-by":"crossref","unstructured":"Luo, R.: BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief. Bioinform. (2022)","DOI":"10.1093\/bib\/bbac409"},{"issue":"2","key":"41_CR12","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1109\/TMI.2018.2865709","volume":"38","author":"P Naylor","year":"2018","unstructured":"Naylor, P., et al.: Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans. Med. Imaging 38(2), 448\u2013459 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"41_CR13","doi-asserted-by":"crossref","unstructured":"Nazir, S., et\u00a0al.: 3DGeoMeshNet: a multi-scale graph auto-encoder for 3D mesh reconstruction and completion. Neurocomputing 132652 (2026)","DOI":"10.1016\/j.neucom.2026.132652"},{"key":"41_CR14","doi-asserted-by":"crossref","unstructured":"Nazir, S.: Attention-guided U-Net for cell nucleus segmentation in microscopy images. In: Bioimaging 2026, SCITEPRESS (2026)","DOI":"10.5220\/0014571600004070"},{"key":"41_CR15","doi-asserted-by":"crossref","unstructured":"Nazir, S.: Hybrid inception-VIT networks for fine-grained single-cell image classification. In: IEEE International Symposium on Biomedical Imaging (ISBI). IEEE (2026)","DOI":"10.1109\/ISBI61048.2026.11515896"},{"key":"41_CR16","unstructured":"Pinkard, H.: The berkeley single cell computational microscopy (BSCCM) dataset. arXiv preprint arXiv:2402.06191 (2024)"},{"key":"41_CR17","volume":"136","author":"MI Razzak","year":"2021","unstructured":"Razzak, M.I., et al.: Raabin-WBC: a large dataset for white blood cells classification. Comput. Biol. Med. 136, 104650 (2021)","journal-title":"Comput. Biol. Med."},{"key":"41_CR18","doi-asserted-by":"crossref","unstructured":"Rivenson, Y., et\u00a0al.: Phasestain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light: Sci. Appl. 8, 23 (2019)","DOI":"10.1038\/s41377-019-0129-y"},{"issue":"8","key":"41_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e18297","volume":"9","author":"D Ryu","year":"2023","unstructured":"Ryu, D., et al.: Deep learning-based label-free hematology analysis framework using optical diffraction tomography. Heliyon 9(8), e18297 (2023)","journal-title":"Heliyon"},{"key":"41_CR20","unstructured":"Simonyan, K.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)"},{"key":"41_CR21","doi-asserted-by":"crossref","unstructured":"Szegedy, C.: Rethinking the inception architecture for computer vision. arXiv preprint arXiv:1512.00567 (2015)","DOI":"10.1109\/CVPR.2016.308"},{"issue":"1","key":"41_CR22","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s44330-024-00014-3","volume":"1","author":"J Tomkinson","year":"2024","unstructured":"Tomkinson, J., et al.: Toward generalizable phenotype prediction from single-cell morphology representations. BMC Methods 1(1), 17 (2024)","journal-title":"BMC Methods"},{"key":"41_CR23","unstructured":"Valanarasu, J.: MEDT: context gated transformer for medical image segmentation. In: MICCAI (2021)"},{"key":"41_CR24","doi-asserted-by":"crossref","unstructured":"Wang, Q.: ECA-Net: Efficient channel attention for deep convolutional neural networks. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"41_CR25","doi-asserted-by":"crossref","unstructured":"Xing, X.D., et\u00a0al.: Deep-DPC: deep learning-assisted label-free temporal imaging discovery of anti-fibrotic compounds by controlling cell morphology. J. Adv. Res. (2025)","DOI":"10.1016\/j.jare.2025.02.028"},{"key":"41_CR26","doi-asserted-by":"crossref","unstructured":"Yan, B.: Style-aware radiology report generation with radgraph and few-shot prompting. In: EMNLP 2023, pp. 14676\u201314688 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.977"},{"key":"41_CR27","unstructured":"Zhang, W.: Protein expression prediction from imaging flow cytometry using deep learning. Cell Reports Methods (2022)"},{"key":"41_CR28","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101918","volume":"70","author":"L Zhou","year":"2021","unstructured":"Zhou, L., et al.: Multi-task learning for medical image analysis: a survey. Med. Image Anal. 70, 101992 (2021)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-31666-0_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T05:47:12Z","timestamp":1785649632000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-31666-0_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,3]]},"ISBN":["9783032316653","9783032316660"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-31666-0_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,3]]},"assertion":[{"value":"3 August 2026","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":"Lyon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 August 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 August 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}