{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:52:15Z","timestamp":1742914335550,"version":"3.40.3"},"publisher-location":"Cham","reference-count":71,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031734106"},{"type":"electronic","value":"9783031734113"}],"license":[{"start":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T00:00:00Z","timestamp":1732320000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T00:00:00Z","timestamp":1732320000000},"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\/978-3-031-73411-3_2","type":"book-chapter","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T20:06:17Z","timestamp":1732305977000},"page":"18-36","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BugNIST a\u00a0Large Volumetric Dataset for\u00a0Object Detection Under Domain Shift"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8479-4885","authenticated-orcid":false,"given":"Patrick M\u00f8ller","family":"Jensen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6734-5570","authenticated-orcid":false,"given":"Vedrana Andersen","family":"Dahl","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9546-1954","authenticated-orcid":false,"given":"Rebecca","family":"Engberg","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2895-1882","authenticated-orcid":false,"given":"Carsten","family":"Gundlach","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7900-5733","authenticated-orcid":false,"given":"Hans Marin","family":"Kjer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0068-8170","authenticated-orcid":false,"given":"Anders Bjorholm","family":"Dahl","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,23]]},"reference":[{"issue":"4","key":"2_CR1","doi-asserted-by":"publisher","first-page":"044501","DOI":"10.1117\/1.JMI.5.4.044501","volume":"5","author":"SG Armato","year":"2018","unstructured":"Armato, S.G., et al.: PROSTATEx challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. J. Med. Imaging 5(4), 044501\u2013044501 (2018)","journal-title":"J. Med. Imaging"},{"issue":"2","key":"2_CR2","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato","year":"2011","unstructured":"Armato, S.G., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915\u2013931 (2011)","journal-title":"Med. Phys."},{"key":"2_CR3","unstructured":"Baid, U., et\u00a0al.: The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)"},{"key":"2_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1007\/978-3-030-87240-3_51","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"M Baumgartner","year":"2021","unstructured":"Baumgartner, M., J\u00e4ger, P.F., Isensee, F., Maier-Hein, K.H.: nnDetection: a self-configuring method for medical object detection. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 530\u2013539. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_51"},{"key":"2_CR5","unstructured":"Cardoso, M.J., et\u00a0al.: MONAI: An open-source framework for deep learning in healthcare. arXiv preprint arXiv:2211.02701 (2022)"},{"key":"2_CR6","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.compag.2017.12.004","volume":"144","author":"M Charytanowicz","year":"2018","unstructured":"Charytanowicz, M., Kulczycki, P., Kowalski, P.A., \u0141ukasik, S., Czabak-Garbacz, R.: An evaluation of utilizing geometric features for wheat grain classification using X-ray images. Comput. Electron. Agric. 144, 260\u2013268 (2018)","journal-title":"Comput. Electron. Agric."},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Chaudhary, S., Sadbhawna, S., Jakhetiya, V., Subudhi, B.N., Baid, U., Guntuku, S.C.: Detecting COVID-19 and community acquired pneumonia using chest CT scan images with deep learning. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8583\u20138587. IEEE (2021)","DOI":"10.1109\/ICASSP39728.2021.9414007"},{"key":"2_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"issue":"3","key":"2_CR10","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/aa9c19","volume":"29","author":"F De Carlo","year":"2018","unstructured":"De Carlo, F., et al.: TomoBank: a tomographic data repository for computational x-ray science. Meas. Sci. Technol. 29(3), 034004 (2018)","journal-title":"Meas. Sci. Technol."},{"key":"2_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102628","volume":"83","author":"R Dorent","year":"2023","unstructured":"Dorent, R., et al.: CrossMoDA 2021 challenge: benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation. Med. Image Anal. 83, 102628 (2023)","journal-title":"Med. Image Anal."},{"issue":"10","key":"2_CR12","doi-asserted-by":"publisher","first-page":"3146","DOI":"10.1002\/fsn3.1179","volume":"7","author":"Z Du","year":"2019","unstructured":"Du, Z., Hu, Y., Ali Buttar, N., Mahmood, A.: X-ray computed tomography for quality inspection of agricultural products: a review. Food Sci. Nutr. 7(10), 3146\u20133160 (2019)","journal-title":"Food Sci. Nutr."},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.foodcont.2016.02.023","volume":"67","author":"H Einarsd\u00f3ttir","year":"2016","unstructured":"Einarsd\u00f3ttir, H., et al.: Novelty detection of foreign objects in food using multi-modal X-ray imaging. Food Control 67, 39\u201347 (2016)","journal-title":"Food Control"},{"key":"2_CR14","doi-asserted-by":"publisher","unstructured":"European Organization For Nuclear Research, OpenAIRE: Zenodo (2013). https:\/\/doi.org\/10.25495\/7GXK-RD71, https:\/\/www.zenodo.org\/","DOI":"10.25495\/7GXK-RD71"},{"issue":"8","key":"2_CR15","doi-asserted-by":"publisher","first-page":"2489","DOI":"10.1016\/j.patcog.2015.02.006","volume":"48","author":"G Flitton","year":"2015","unstructured":"Flitton, G., Mouton, A., Breckon, T.P.: Object classification in 3D baggage security computed tomography imagery using visual codebooks. Pattern Recogn. 48(8), 2489\u20132499 (2015)","journal-title":"Pattern Recogn."},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"issue":"3","key":"2_CR18","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","volume":"69","author":"H Guan","year":"2021","unstructured":"Guan, H., Liu, M.: Domain adaptation for medical image analysis: a survey. IEEE Trans. Biomed. Eng. 69(3), 1173\u20131185 (2021)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"1","key":"2_CR19","doi-asserted-by":"publisher","first-page":"4080","DOI":"10.1038\/s41467-020-17971-2","volume":"11","author":"SA Harmon","year":"2020","unstructured":"Harmon, S.A., et al.: Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nat. Commun. 11(1), 4080 (2020)","journal-title":"Nat. Commun."},{"issue":"1","key":"2_CR20","doi-asserted-by":"publisher","first-page":"13863","DOI":"10.1038\/s41598-020-70970-7","volume":"10","author":"CA Hipsley","year":"2020","unstructured":"Hipsley, C.A., Aguilar, R., Black, J.R., Hocknull, S.A.: High-throughput microCT scanning of small specimens: preparation, packing, parameters and post-processing. Sci. Rep. 10(1), 13863 (2020)","journal-title":"Sci. Rep."},{"issue":"2","key":"2_CR21","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"2_CR22","unstructured":"Jaeger, P.F., et al.: Retina U-Net: Embarrassingly simple exploitation of segmentation supervision for medical object detection. In: Machine Learning for Health Workshop, pp. 171\u2013183. PMLR (2020)"},{"key":"2_CR23","unstructured":"Jain, Y., et al.: SenNet + HOA - hacking the human vasculature in 3D (2023). https:\/\/kaggle.com\/competitions\/blood-vessel-segmentation"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Jarolmasjed, S., Espinoza, C.Z., Sankaran, S., Khot, L.R.: Postharvest bitter pit detection and progression evaluation in \u2018Honeycrisp\u2019 apples using computed tomography images. Postharvest Biol. Technol. 118, 35\u201342 (2016)","DOI":"10.1016\/j.postharvbio.2016.03.014"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Jeppesen, N., Christensen, A.N., Dahl, V.A., Dahl, A.B.: Sparse layered graphs for multi-object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12777\u201312785 (2020)","DOI":"10.1109\/CVPR42600.2020.01279"},{"key":"2_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2020.103106","volume":"62","author":"L Jin","year":"2020","unstructured":"Jin, L., et al.: Deep-learning-assisted detection and segmentation of rib fractures from CT scans: development and validation of FracNet. EBioMedicine 62, 103106 (2020)","journal-title":"EBioMedicine"},{"issue":"9","key":"2_CR27","doi-asserted-by":"publisher","first-page":"2167","DOI":"10.3390\/s19092167","volume":"19","author":"J Ker","year":"2019","unstructured":"Ker, J., Singh, S.P., Bai, Y., Rao, J., Lim, T., Wang, L.: Image thresholding improves 3-dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans. Sensors 19(9), 2167 (2019)","journal-title":"Sensors"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"issue":"11","key":"2_CR29","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Deeper, broader and artier domain generalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5542\u20135550 (2017)","DOI":"10.1109\/ICCV.2017.591"},{"key":"2_CR31","unstructured":"Li, Y., Xie, S., Chen, X., Dollar, P., He, K., Girshick, R.: Benchmarking detection transfer learning with vision transformers. arXiv preprint arXiv:2111.11429 (2021)"},{"key":"2_CR32","unstructured":"Li, Y., Fan, Y.: Medical image segmentation with domain adaptation: A survey. arXiv preprint arXiv:2311.01702 (2023)"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"2_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"issue":"1","key":"2_CR35","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15(1), 654 (2024)","journal-title":"Nat. Commun."},{"issue":"1","key":"2_CR36","doi-asserted-by":"publisher","first-page":"5217","DOI":"10.1038\/s41467-018-07619-7","volume":"9","author":"L Maier-Hein","year":"2018","unstructured":"Maier-Hein, L., et al.: Why rankings of biomedical image analysis competitions should be interpreted with care. Nat. Commun. 9(1), 5217 (2018)","journal-title":"Nat. Commun."},{"key":"2_CR37","unstructured":"Muandet, K., Balduzzi, D., Sch\u00f6lkopf, B.: Domain generalization via invariant feature representation. In: International Conference on Machine Learning, pp. 10\u201318. PMLR (2013)"},{"issue":"1","key":"2_CR38","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1137\/0105003","volume":"5","author":"J Munkres","year":"1957","unstructured":"Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 5(1), 32\u201338 (1957)","journal-title":"J. Soc. Ind. Appl. Math."},{"key":"2_CR39","doi-asserted-by":"publisher","unstructured":"OECD: Health at a Glance 2023 (2023). https:\/\/doi.org\/10.1787\/7a7afb35-en","DOI":"10.1787\/7a7afb35-en"},{"issue":"2","key":"2_CR40","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Networks 22(2), 199\u2013210 (2010)","journal-title":"IEEE Trans. Neural Networks"},{"key":"2_CR41","doi-asserted-by":"crossref","unstructured":"Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1406\u20131415 (2019)","DOI":"10.1109\/ICCV.2019.00149"},{"issue":"1","key":"2_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12915-020-0753-2","volume":"18","author":"SD Rawson","year":"2020","unstructured":"Rawson, S.D., Maksimcuka, J., Withers, P.J., Cartmell, S.H.: X-ray computed tomography in life sciences. BMC Biol. 18(1), 1\u201315 (2020)","journal-title":"BMC Biol."},{"key":"2_CR43","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"2_CR44","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 28 (2015)"},{"key":"2_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/978-3-319-46475-6_7","volume-title":"Computer Vision \u2013 ECCV 2016","author":"SR Richter","year":"2016","unstructured":"Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: ground truth from computer games. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 102\u2013118. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_7"},{"key":"2_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"2_CR47","doi-asserted-by":"crossref","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: The synthia dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3234\u20133243 (2016)","DOI":"10.1109\/CVPR.2016.352"},{"key":"2_CR48","doi-asserted-by":"crossref","unstructured":"Sankaranarayanan, S., Balaji, Y., Jain, A., Lim, S.N., Chellappa, R.: Learning from synthetic data: addressing domain shift for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3752\u20133761 (2018)","DOI":"10.1109\/CVPR.2018.00395"},{"key":"2_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104306","volume":"132","author":"S Serte","year":"2021","unstructured":"Serte, S., Demirel, H.: Deep learning for diagnosis of COVID-19 using 3D CT scans. Comput. Biol. Med. 132, 104306 (2021)","journal-title":"Comput. Biol. Med."},{"key":"2_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2017.06.015","volume":"42","author":"AAA Setio","year":"2017","unstructured":"Setio, A.A.A., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge. Med. Image Anal. 42, 1\u201313 (2017)","journal-title":"Med. Image Anal."},{"key":"2_CR51","unstructured":"Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. CoRR abs\/1902.09063 (2019). http:\/\/arxiv.org\/abs\/1902.09063"},{"key":"2_CR52","doi-asserted-by":"crossref","unstructured":"Sun, T., et al.: SHIFT: a synthetic driving dataset for continuous multi-task domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21371\u201321382 (2022)","DOI":"10.1109\/CVPR52688.2022.02068"},{"key":"2_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118216","volume":"238","author":"KM Timmins","year":"2021","unstructured":"Timmins, K.M., et al.: Comparing methods of detecting and segmenting unruptured intracranial aneurysms on TOF-MRAS: the ADAM challenge. Neuroimage 238, 118216 (2021)","journal-title":"Neuroimage"},{"key":"2_CR54","unstructured":"TorchVision maintainers and contributors: Torchvision: Pytorch\u2019s computer vision library. https:\/\/github.com\/pytorch\/vision (2016)"},{"key":"2_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.marpolbul.2023.115350","volume":"194","author":"MM Trusler","year":"2023","unstructured":"Trusler, M.M., Sturrock, C.J., Vane, C.H., Cook, S., Lomax, B.H.: X-ray computed tomography: a novel non-invasive approach for the detection of microplastics in sediments? Mar. Pollut. Bull. 194, 115350 (2023)","journal-title":"Mar. Pollut. Bull."},{"key":"2_CR56","doi-asserted-by":"publisher","unstructured":"Velayudhan, D., Hassan, T., Damiani, E., Werghi, N.: Recent advances in baggage threat detection: a comprehensive and systematic survey. ACM Comput. Surv. 55(8), 1\u201338 (2022). https:\/\/doi.org\/10.1145\/3549932","DOI":"10.1145\/3549932"},{"key":"2_CR57","doi-asserted-by":"crossref","unstructured":"Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5018\u20135027 (2017)","DOI":"10.1109\/CVPR.2017.572"},{"issue":"12","key":"2_CR58","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1038\/s41592-021-01317-x","volume":"18","author":"C Walsh","year":"2021","unstructured":"Walsh, C., et al.: Imaging intact human organs with local resolution of cellular structures using hierarchical phase-contrast tomography. Nat. Methods 18(12), 1532\u20131541 (2021)","journal-title":"Nat. Methods"},{"key":"2_CR59","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","volume":"312","author":"M Wang","year":"2018","unstructured":"Wang, M., Deng, W.: Deep visual domain adaptation: a survey. Neurocomputing 312, 135\u2013153 (2018)","journal-title":"Neurocomputing"},{"key":"2_CR60","doi-asserted-by":"crossref","unstructured":"Wang, M., Liu, Y., Yuan, J., Wang, S., Wang, Z., Wang, W.: Inter-class and inter-domain semantic augmentation for domain generalization. IEEE Trans. Image Process. 33, 1338\u20131347 (2024)","DOI":"10.1109\/TIP.2024.3354420"},{"key":"2_CR61","doi-asserted-by":"crossref","unstructured":"Wang, Q., Bhowmik, N., Breckon, T.P.: Multi-class 3D object detection within volumetric 3D computed tomography baggage security screening imagery. In: Proceedings of the IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 13\u201318. IEEE (2020)","DOI":"10.1109\/ICMLA51294.2020.00012"},{"key":"2_CR62","doi-asserted-by":"crossref","unstructured":"Wang, Q., Breckon, T.P.: Contraband materials detection within volumetric 3D computed tomography baggage security screening imagery. In: Proceedings of the IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 75\u201382. IEEE (2021)","DOI":"10.1109\/ICMLA52953.2021.00020"},{"issue":"1","key":"2_CR63","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s43586-021-00015-4","volume":"1","author":"PJ Withers","year":"2021","unstructured":"Withers, P.J., et al.: X-ray computed tomography. Nat. Rev. Methods Primers 1(1), 18 (2021)","journal-title":"Nat. Rev. Methods Primers"},{"key":"2_CR64","doi-asserted-by":"crossref","unstructured":"Wu, D., et\u00a0al.: A deep learning-integrated micro-CT image analysis pipeline for quantifying rice lodging resistance-related traits. Plant Commun. 2(2), 100165 (2021)","DOI":"10.1016\/j.xplc.2021.100165"},{"issue":"11","key":"2_CR65","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.3390\/sym12111787","volume":"12","author":"Z Xiao","year":"2020","unstructured":"Xiao, Z., Liu, B., Geng, L., Zhang, F., Liu, Y.: Segmentation of lung nodules using improved 3D-UNet neural network. Symmetry 12(11), 1787 (2020)","journal-title":"Symmetry"},{"key":"2_CR66","doi-asserted-by":"crossref","unstructured":"Xie, Y., Ji, Q.: A new efficient ellipse detection method. In: Proceedings of the International Conference on Pattern Recognition (ICPR). vol.\u00a02, pp. 957\u2013960. IEEE (2002)","DOI":"10.1109\/ICPR.2002.1048464"},{"issue":"2","key":"2_CR67","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1037\/0033-295X.114.2.245","volume":"114","author":"F Xu","year":"2007","unstructured":"Xu, F., Tenenbaum, J.B.: Word learning as bayesian inference. Psychol. Rev. 114(2), 245 (2007)","journal-title":"Psychol. Rev."},{"key":"2_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2021.104871","volume":"156","author":"YT Yan","year":"2021","unstructured":"Yan, Y.T., Chua, S., DeCarlo, T.M., Kempf, P., Morgan, K.M., Switzer, A.D.: Core-CT: a MATLAB application for the quantitative analysis of sediment and coral cores from X-ray computed tomography (CT). Comput. Geosci. 156, 104871 (2021)","journal-title":"Comput. Geosci."},{"key":"2_CR69","unstructured":"Zhao, W.X., et\u00a0al.: A survey of large language models. arXiv preprint arXiv:2303.18223 (2023)"},{"key":"2_CR70","doi-asserted-by":"publisher","unstructured":"Zhou, K., Yang, Y., Hospedales, T., Xiang, T.: Deep domain-adversarial image generation for domain generalisation. Proc. AAAI Conf. Artif. Intell. 34(07), 13025\u201313032 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i07.7003, https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/7003","DOI":"10.1609\/aaai.v34i07.7003"},{"issue":"5","key":"2_CR71","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1109\/JPROC.2021.3054390","volume":"109","author":"SK Zhou","year":"2021","unstructured":"Zhou, S.K., et al.: A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises. Proc. IEEE 109(5), 820\u2013838 (2021)","journal-title":"Proc. IEEE"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73411-3_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T21:22:34Z","timestamp":1732310554000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73411-3_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,23]]},"ISBN":["9783031734106","9783031734113"],"references-count":71,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73411-3_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,23]]},"assertion":[{"value":"23 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}