{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:28:11Z","timestamp":1742974091533,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031731570"},{"type":"electronic","value":"9783031731587"}],"license":[{"start":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:00:00Z","timestamp":1727913600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:00:00Z","timestamp":1727913600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73158-7_3","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T23:02:04Z","timestamp":1727910124000},"page":"23-33","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FISHing in\u00a0Uncertainty: Synthetic Contrastive Learning for\u00a0Genetic Aberration Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8406-0736","authenticated-orcid":false,"given":"Simon","family":"Gutwein","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5217-2854","authenticated-orcid":false,"given":"Martin","family":"Kampel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1439-5301","authenticated-orcid":false,"given":"Sabine","family":"Taschner-Mandl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9066-4473","authenticated-orcid":false,"given":"Roxane","family":"Licandro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Algan, G., Ulusoy, I.: Image classification with deep learning in the presence of noisy labels: a survey. Knowl. Based Syst. 215, 106771 (2021)","DOI":"10.1016\/j.knosys.2021.106771"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Ambros, P.F., et al.: International consensus for neuroblastoma molecular diagnostics: report from the international neuroblastoma risk group (INRG) biology committee. Br. J. Cancer 100(9), 1471\u20131482 (2009)","DOI":"10.1038\/sj.bjc.6605014"},{"key":"3_CR3","unstructured":"Ardeshir, S., Azizan, N.: Uncertainty in contrastive learning: on the predictability of downstream performance (2022)"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Bahry, E., et al.: RS-FISH: precise, interactive, fast, and scalable FISH spot detection. bioRxiv pp. 2021.03.09.434205 (2021)","DOI":"10.1101\/2021.03.09.434205"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Bouilhol, E., Lefevre, E., Dartigues, B., Brackin, R., Savulescu, A.F., Nikolski, M.: DeepSpot: a deep neural network for RNA spot enhancement in smfish microscopy images (2021)","DOI":"10.1101\/2021.11.25.469984"},{"key":"3_CR6","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020)"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Cohn, S.L., et al.: The international neuroblastoma risk group (INRG) classification system: An INRG task force report. J. Clin. Oncol. 27, 289\u2013297 (1 2009)","DOI":"10.1200\/JCO.2008.16.6785"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Ding, Y., Liu, J., Xiong, J., Shi, Y.: Revisiting the evaluation of uncertainty estimation and its application to explore model complexity-uncertainty trade-off. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2020-June, pp. 22\u201331 (2019)","DOI":"10.1109\/CVPRW50498.2020.00010"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Eichenberger, B.T., Zhan, Y., Rempfler, M., Giorgetti, L., Chao, J.A.: DeepBlink: threshold-independent detection and localization of diffraction-limited spots. Nucleic Acids Res. 49, 7292\u20137297 (2021)","DOI":"10.1093\/nar\/gkab546"},{"key":"3_CR10","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: representing model uncertainty in deep learning (6 2015)"},{"key":"3_CR11","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1101\/sqb.2017.82.033761","volume":"82","author":"PR Gudla","year":"2017","unstructured":"Gudla, P.R., Nakayama, K., Pegoraro, G., Misteli, T.: SpotLearn: convolutional neural network for detection of fluorescence in situ hybridization (FISH) signals in high-throughput imaging approaches. Cold Spring Harb. Symp. Quant. Biol. 82, 57\u201370 (2017)","journal-title":"Cold Spring Harb. Symp. Quant. Biol."},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Gutwein, S., Kampel, M., Sabine, T.M., Licandro, R.: Genuine: genomic and nucleus information embedding for single cell genetic alteration classification in microscopic images. In: Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods (2024)","DOI":"10.5220\/0012319700003654"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, pp. 770\u2013778 (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Imbert, A., Mueller, F., Walter, T.: PointFISH \u2013 learning point cloud representations for RNA localization patterns (2023)","DOI":"10.1007\/978-3-031-25069-9_32"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Imbert, A., et al.: Fish-quant v2: a scalable and modular tool for smFISH image analysis. RNA 28, 786\u2013795 (6 2022)","DOI":"10.1261\/rna.079073.121"},{"key":"3_CR16","unstructured":"Kirchhof, M., Kasneci, E., Oh, S.J.: Probabilistic contrastive learning recovers the correct aleatoric uncertainty of ambiguous inputs (2023)"},{"key":"3_CR17","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles (2016)"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42, 318\u2013327 (8 2017)","DOI":"10.1109\/TPAMI.2018.2858826"},{"key":"3_CR19","unstructured":"Linmans, J., van\u00a0der Laak, J., Litjens, G.: Efficient out-of-distribution detection in digital pathology using multi-head convolutional neural networks (2020)"},{"key":"3_CR20","unstructured":"McInnes, L., Healy, J., Melville, J.: UMAP: uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426 [cs, stat] (2020), comment: Reference implementation available at http:\/\/github.com\/lmcinnes\/umap"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Mukhoti, J., Kirsch, A., Amersfoort, J.V., Torr, P.H.S., Gal, Y.: Deep deterministic uncertainty: a new simple baseline (2023)","DOI":"10.1109\/CVPR52729.2023.02336"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Penault-Llorca, F., et al.: Emerging technologies for assessing HER2 amplification. Am. J. Clin. Pathol. 132, 539\u2013548 (2009)","DOI":"10.1309\/AJCPV2I0HGPMGBSQ"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Seoni, S., Jahmunah, V., Salvi, M., Barua, P.D., Molinari, F., Acharya, U.R.: Application of uncertainty quantification to artificial intelligence in healthcare: a review of last decade (2013-2023). Comput. Biol. Med. 107441 (2023)","DOI":"10.1016\/j.compbiomed.2023.107441"},{"key":"3_CR24","unstructured":"Sohn, K.: Improved deep metric learning with multi-class n-pair loss objective. Adv. Neural Inf. Process. Syst. 29 (2016)"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Tang, Z., Wang, L., Tang, G., Medeiros, L.J.: Fluorescence in situ hybridization (FISH) for detecting anaplastic lymphoma kinase (ALK) rearrangement in lung cancer: Clinically relevant technical aspects. Int. j. mol. sci. 20(16), 3939 (2019)","DOI":"10.3390\/ijms20163939"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Tinevez, J.Y., et al.: TrackMate: an open and extensible platform for single-particle tracking. Methods 115, 80\u201390 (2017)","DOI":"10.1016\/j.ymeth.2016.09.016"},{"key":"3_CR27","unstructured":"Wei, H., Xie, R., Cheng, H., Feng, L., An, B., Li, Y.: Mitigating neural network overconfidence with logit normalization (2022)"},{"key":"3_CR28","unstructured":"Winkens, J., et al.: Contrastive training for improved out-of-distribution detection (2020)"},{"key":"3_CR29","doi-asserted-by":"crossref","unstructured":"Wollmann, T., Ritter, C., Dohrke, J.N., Lee, J.Y., Bartenschlager, R., Rohr, K.: DetNet: deep neural network for particle detection in fluorescence microscopy images. In: Proceedings - International Symposium on Biomedical Imaging 2019-April, pp. 517\u2013520 (2019)","DOI":"10.1109\/ISBI.2019.8759234"},{"key":"3_CR30","unstructured":"Wu, M., Goodman, N.: A simple framework for uncertainty in contrastive learning (2020)"},{"key":"3_CR31","doi-asserted-by":"crossref","unstructured":"Zakrzewski, F., et al.: Automated detection of the HER2 gene amplification status in fluorescence in situ hybridization images for the diagnostics of cancer tissues. Sci. Rep. 9(1), 8231 (2019)","DOI":"10.1038\/s41598-019-44643-z"}],"container-title":["Lecture Notes in Computer Science","Uncertainty for Safe Utilization of Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73158-7_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T23:06:56Z","timestamp":1727910416000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73158-7_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,3]]},"ISBN":["9783031731570","9783031731587"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73158-7_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,3]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"UNSURE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"unsure2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/unsuremiccai.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}