{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T22:53:40Z","timestamp":1783032820085,"version":"3.54.6"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T00:00:00Z","timestamp":1687392000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T00:00:00Z","timestamp":1687392000000},"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":["Nat Mach Intell"],"DOI":"10.1038\/s42256-023-00677-7","type":"journal-article","created":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T21:31:04Z","timestamp":1687469464000},"page":"656-668","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["A super-resolution strategy for mass spectrometry imaging via transfer learning"],"prefix":"10.1038","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0231-4401","authenticated-orcid":false,"given":"Tiepeng","family":"Liao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihao","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoliang","family":"Chai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Man","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenjian","family":"Miao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjie","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4978-1974","authenticated-orcid":false,"given":"Qi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhilin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenwei","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1184-8552","authenticated-orcid":false,"given":"Longfeng","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1822-3731","authenticated-orcid":false,"given":"Zilei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1585-3749","authenticated-orcid":false,"given":"Wei","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1245-817X","authenticated-orcid":false,"given":"Hongying","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,22]]},"reference":[{"key":"677_CR1","doi-asserted-by":"crossref","first-page":"3946","DOI":"10.1016\/j.chroma.2010.01.033","volume":"1217","author":"ER Amstalden van Hove","year":"2010","unstructured":"Amstalden van Hove, E. R., Smith, D. F. & Heeren, R. M. A. A concise review of mass spectrometry imaging. J. Chromatogr. A 1217, 3946\u20133954 (2010).","journal-title":"J. Chromatogr. A"},{"key":"677_CR2","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1021\/acs.analchem.7b04733","volume":"90","author":"AR Buchberger","year":"2018","unstructured":"Buchberger, A. R., DeLaney, K., Johnson, J. & Li, L. Mass spectrometry imaging: a review of emerging advancements and future insights. Anal. Chem. 90, 240\u2013265 (2018).","journal-title":"Anal. Chem."},{"key":"677_CR3","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1002\/tcr.201700027","volume":"18","author":"RL Hansen","year":"2018","unstructured":"Hansen, R. L. & Lee, Y. J. High-spatial resolution mass spectrometry imaging: toward single cell metabolomics in plant tissues. Chem. Rec. 18, 65\u201377 (2018).","journal-title":"Chem. Rec."},{"key":"677_CR4","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s00418-013-1097-6","volume":"139","author":"A R\u00f6mpp","year":"2013","unstructured":"R\u00f6mpp, A. & Spengler, B. Mass spectrometry imaging with high resolution in mass and space. Histochem. Cell Biol. 139, 759\u2013783 (2013).","journal-title":"Histochem. Cell Biol."},{"key":"677_CR5","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1002\/rcm.2397","volume":"20","author":"Y Hsieh","year":"2006","unstructured":"Hsieh, Y. et al. Matrix-assisted laser desorption\/ionization imaging mass spectrometry for direct measurement of clozapine in rat brain tissue. Rapid Commun. Mass Spectrom. 20, 965\u2013972 (2006).","journal-title":"Rapid Commun. Mass Spectrom."},{"key":"677_CR6","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1002\/rcm.1725","volume":"18","author":"J Bunch","year":"2004","unstructured":"Bunch, J., Clench, M. R. & Richards, D. S. Determination of pharmaceutical compounds in skin by imaging matrix-assisted laser desorption\/ionisation mass spectrometry. Rapid Commun. Mass Spectrom. 18, 3051\u20133060 (2004).","journal-title":"Rapid Commun. Mass Spectrom."},{"key":"677_CR7","doi-asserted-by":"crossref","first-page":"4999","DOI":"10.1016\/j.jprot.2012.07.028","volume":"75","author":"B Prideaux","year":"2012","unstructured":"Prideaux, B. & Stoeckli, M. Mass spectrometry imaging for drug distribution studies. J. Proteom. 75, 4999\u20135013 (2012).","journal-title":"J. Proteom."},{"key":"677_CR8","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1126\/science.1104404","volume":"306","author":"Z Tak\u00e1ts","year":"2004","unstructured":"Tak\u00e1ts, Z., Wiseman Justin, M., Gologan, B. & Cooks, R. G. Mass spectrometry sampling under ambient conditions with desorption electrospray ionization. Science 306, 471\u2013473 (2004).","journal-title":"Science"},{"key":"677_CR9","doi-asserted-by":"crossref","first-page":"18120","DOI":"10.1073\/pnas.0801066105","volume":"105","author":"M Wiseman Justin","year":"2008","unstructured":"Wiseman Justin, M. et al. Desorption electrospray ionization mass spectrometry: imaging drugs and metabolites in tissues. Proc. Natl Acad. Sci. USA 105, 18120\u201318125 (2008).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"677_CR10","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1021\/acs.chemrestox.5b00262","volume":"28","author":"A Nilsson","year":"2015","unstructured":"Nilsson, A. et al. Investigating nephrotoxicity of polymyxin derivatives by mapping renal distribution using mass spectrometry imaging. Chem. Res. Toxicol. 28, 1823\u20131830 (2015).","journal-title":"Chem. Res. Toxicol."},{"key":"677_CR11","doi-asserted-by":"crossref","first-page":"2693","DOI":"10.1111\/pce.13395","volume":"41","author":"B Li","year":"2018","unstructured":"Li, B. et al. Interrogation of spatial metabolome of Ginkgo biloba with high-resolution matrix-assisted laser desorption\/ionization and laser desorption\/ionization mass spectrometry imaging. Plant Cell Environ. 41, 2693\u20132703 (2018).","journal-title":"Plant Cell Environ."},{"key":"677_CR12","doi-asserted-by":"crossref","first-page":"11576","DOI":"10.1021\/ac402777k","volume":"85","author":"DF Cobice","year":"2013","unstructured":"Cobice, D. F. et al. Mass spectrometry imaging for dissecting steroid intracrinology within target tissues. Anal. Chem. 85, 11576\u201311584 (2013).","journal-title":"Anal. Chem."},{"key":"677_CR13","doi-asserted-by":"crossref","first-page":"3445","DOI":"10.1038\/s41596-019-0237-4","volume":"14","author":"R Yin","year":"2019","unstructured":"Yin, R., Burnum-Johnson, K. E., Sun, X., Dey, S. K. & Laskin, J. High spatial resolution imaging of biological tissues using nanospray desorption electrospray ionization mass spectrometry. Nat. Protoc. 14, 3445\u20133470 (2019).","journal-title":"Nat. Protoc."},{"key":"677_CR14","doi-asserted-by":"crossref","first-page":"122380","DOI":"10.1016\/j.talanta.2021.122380","volume":"231","author":"K Qi","year":"2021","unstructured":"Qi, K. et al. Cholesterol was identified as a biomarker in human melanocytic nevi using DESI and DESI\/PI mass spectrometry imaging. Talanta 231, 122380 (2021).","journal-title":"Talanta"},{"key":"677_CR15","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1038\/nrurol.2017.46","volume":"14","author":"C Thoma","year":"2017","unstructured":"Thoma, C. Making DESI-MSI desirable. Nat. Rev. Urol. 14, 325\u2013325 (2017).","journal-title":"Nat. Rev. Urol."},{"key":"677_CR16","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.jmsacl.2021.12.006","volume":"23","author":"B Balluff","year":"2022","unstructured":"Balluff, B., Heeren, R. M. A. & Race, A. M. An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities. J. Mass Spectrom. Adv. Clin. Lab 23, 26\u201338 (2022).","journal-title":"J. Mass Spectrom. Adv. Clin. Lab"},{"key":"677_CR17","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s13361-017-1809-6","volume":"29","author":"MT Bokhart","year":"2018","unstructured":"Bokhart, M. T., Nazari, M., Garrard, K. P. & Muddiman, D. C. MSiReader v1.0: evolving open-source mass spectrometry imaging software for targeted and untargeted analyses. J. Am. Soc. Mass. Spectrom. 29, 8\u201316 (2018).","journal-title":"J. Am. Soc. Mass. Spectrom."},{"key":"677_CR18","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1038\/nmeth.3296","volume":"12","author":"R Van de Plas","year":"2015","unstructured":"Van de Plas, R., Yang, J., Spraggins, J. & Caprioli, R. M. Image fusion of mass spectrometry and microscopy: a multimodality paradigm for molecular tissue mapping. Nat. Methods 12, 366\u2013372 (2015).","journal-title":"Nat. Methods"},{"key":"677_CR19","doi-asserted-by":"crossref","first-page":"6125","DOI":"10.1021\/acs.analchem.5b00700","volume":"87","author":"SJ Van Malderen","year":"2015","unstructured":"Van Malderen, S. J., van Elteren, J. T. & Vanhaecke, F. Submicrometer imaging by laser ablation-inductively coupled plasma mass spectrometry via signal and image deconvolution approaches. Anal. Chem. 87, 6125\u20136132 (2015).","journal-title":"Anal. Chem."},{"key":"677_CR20","doi-asserted-by":"crossref","first-page":"14879","DOI":"10.1021\/acs.analchem.9b02380","volume":"91","author":"MT Westerhausen","year":"2019","unstructured":"Westerhausen, M. T. et al. Super-resolution reconstruction for two- and three-dimensional LA-ICP-MS bioimaging. Anal. Chem. 91, 14879\u201314886 (2019).","journal-title":"Anal. Chem."},{"key":"677_CR21","unstructured":"Titus, J. & Geroge, S. A comparison study on different interpolation methods based on satellite images. Int. J. Eng. Res. Technol. 2, 82\u201385 (2013)."},{"key":"677_CR22","unstructured":"Dianyuan, H. Comparison of commonly used image interpolation methods. In Proc. 2nd International Conference on Computer Science and Electronics Engineering 10, 1556\u20131559 (Atlantis Press, 2013)."},{"key":"677_CR23","first-page":"100151","volume":"2","author":"C Zhao","year":"2021","unstructured":"Zhao, C., Guo, L., Dong, J. & Cai, Z. Mass spectrometry imaging-based multi-modal technique: next-generation of biochemical analysis strategy. Innovation 2, 100151\u2013100151 (2021).","journal-title":"Innovation"},{"key":"677_CR24","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1007\/s11307-018-1267-y","volume":"20","author":"T Porta Siegel","year":"2018","unstructured":"Porta Siegel, T. et al. Mass spectrometry imaging and integration with other imaging modalities for greater molecular understanding of biological tissues. Mol. Imag. Biol. 20, 888\u2013901 (2018).","journal-title":"Mol. Imag. Biol."},{"key":"677_CR25","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1002\/mas.21661","volume":"41","author":"K DeLaney","year":"2022","unstructured":"DeLaney, K., Phetsanthad, A. & Li, L. Advances in high-resolution maldi mass spectrometry for neurobiology. Mass Spectrom. Rev. 41, 194\u2013214 (2022).","journal-title":"Mass Spectrom. Rev."},{"key":"677_CR26","doi-asserted-by":"crossref","first-page":"e1229","DOI":"10.1002\/cnr2.1229","volume":"2","author":"M Holzlechner","year":"2019","unstructured":"Holzlechner, M., Eugenin, E. & Prideaux, B. Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer. Cancer Rep. 2, e1229\u2013e1229 (2019).","journal-title":"Cancer Rep."},{"key":"677_CR27","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1007\/s13361-011-0121-0","volume":"22","author":"JM Spraggins","year":"2011","unstructured":"Spraggins, J. M. & Caprioli, R. M. High-speed MALDI-TOF imaging mass spectrometry: rapid ion image acquisition and considerations for next generation instrumentation. J. Am. Soc. Mass. Spectrom. 22, 1022\u20131031 (2011).","journal-title":"J. Am. Soc. Mass. Spectrom."},{"key":"677_CR28","unstructured":"Batson, J. & Royer, L. Noise2Self: blind denoising by self-supervision. In Proc. 36th International Conference on Machine Learning Vol. 97 (eds. Kamalika, C. & Ruslan, S.) 524\u2013533 (PMLR, 2019)."},{"key":"677_CR29","doi-asserted-by":"crossref","unstructured":"Krull, A., Buchholz, T.-O. & Jug, F. Noise2Void-learning denoising from single noisy images. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2129\u20132137 (IEEE, 2019).","DOI":"10.1109\/CVPR.2019.00223"},{"key":"677_CR30","doi-asserted-by":"crossref","first-page":"i284","DOI":"10.1093\/bioinformatics\/bty241","volume":"34","author":"Y Li","year":"2018","unstructured":"Li, Y. et al. DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy. Bioinformatics 34, i284\u2013i294 (2018).","journal-title":"Bioinformatics"},{"key":"677_CR31","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1038\/s41592-018-0216-7","volume":"15","author":"M Weigert","year":"2018","unstructured":"Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods 15, 1090\u20131097 (2018).","journal-title":"Nat. Methods"},{"key":"677_CR32","doi-asserted-by":"crossref","unstructured":"Buchholz, T., Jordan, M., Pigino, G. & Jug, F. Cryo-CARE: content-aware image restoration for cryo-transmission electron microscopy data. In Proc. IEEE 16th International Symposium on Biomedical Imaging 502\u2013506 (IEEE, 2019).","DOI":"10.1109\/ISBI.2019.8759519"},{"key":"677_CR33","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-019-48444-2","volume":"9","author":"K de Haan","year":"2019","unstructured":"de Haan, K., Ballard, Z. S., Rivenson, Y., Wu, Y. & Ozcan, A. Resolution enhancement in scanning electron microscopy using deep learning. Sci Rep. 9, 12050 (2019).","journal-title":"Sci Rep."},{"key":"677_CR34","doi-asserted-by":"crossref","unstructured":"Heinrich, L., Bogovic, J. A. & Saalfeld, S. in Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017, Vol. 10434 (eds Descoteaux, M. et al.) 135\u2013143 (Springer, Cham, 2017).","DOI":"10.1007\/978-3-319-66185-8_16"},{"key":"677_CR35","doi-asserted-by":"crossref","unstructured":"Sreehari, S. et al. Multi-resolution data fusion for super-resolution electron microscopy. In Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 1084\u20131092 (IEEE, 2017).","DOI":"10.1109\/CVPRW.2017.146"},{"key":"677_CR36","doi-asserted-by":"crossref","first-page":"145011","DOI":"10.1088\/1361-6560\/aacdd4","volume":"63","author":"J Park","year":"2018","unstructured":"Park, J. et al. Computed tomography super-resolution using deep convolutional neural network. Phys. Med. Biol. 63, 145011 (2018).","journal-title":"Phys. Med. Biol."},{"key":"677_CR37","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/TMI.2019.2922960","volume":"39","author":"C You","year":"2020","unstructured":"You, C. et al. CT Super-Resolution GAN constrained by the identical, residual, and cycle learning ensemble (GAN-CIRCLE). IEEE Trans. Med. Imag. 39, 188\u2013203 (2020).","journal-title":"IEEE Trans. Med. Imag."},{"key":"677_CR38","unstructured":"Zhang, Z. et al. CT super resolution via zero shot learning. Preprint at https:\/\/arxiv.org\/abs\/2012.08943 (2020)."},{"key":"677_CR39","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.inffus.2021.09.005","volume":"79","author":"H Chen","year":"2022","unstructured":"Chen, H. et al. Real-world single image super-resolution: a brief review. Inform. Fusion 79, 124\u2013145 (2022).","journal-title":"Inform. Fusion"},{"key":"677_CR40","doi-asserted-by":"crossref","first-page":"3106","DOI":"10.1109\/TMM.2019.2919431","volume":"21","author":"W Yang","year":"2019","unstructured":"Yang, W. et al. Deep learning for single image super-resolution: a brief review. IEEE Trans. Multimedia 21, 3106\u20133121 (2019).","journal-title":"IEEE Trans. Multimedia"},{"key":"677_CR41","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10994-012-5310-y","volume":"90","author":"L Yang","year":"2013","unstructured":"Yang, L., Hanneke, S. & Carbonell, J. A theory of transfer learning with applications to active learning. Mach. Learn. 90, 161\u2013189 (2013).","journal-title":"Mach. Learn."},{"key":"677_CR42","unstructured":"Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. How transferable are features in deep neural networks? In Proc. 27th International Conference on Neural Information Processing Systems \u2013 Volume 2 3320\u20133328 (MIT Press, 2014)."},{"key":"677_CR43","doi-asserted-by":"crossref","first-page":"1800250","DOI":"10.1002\/advs.201800250","volume":"5","author":"J He","year":"2018","unstructured":"He, J. et al. A sensitive and wide coverage ambient mass spectrometry imaging method for functional metabolites based molecular histology. Adv. Sci. 5, 1800250 (2018).","journal-title":"Adv. Sci."},{"key":"677_CR44","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1073\/pnas.1310524111","volume":"111","author":"KA Veselkov","year":"2014","unstructured":"Veselkov, K. A. et al. Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer. Proc. Natl Acad. Sci. USA 111, 1216\u20131221 (2014).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"677_CR45","doi-asserted-by":"crossref","unstructured":"Wang, X. et al. ESRGAN: enhanced super-resolution generative adversarial networks. In Proc. European Conference on Computer Vision \u2013 ECCV 2018 Workshops (eds Leal-Taix\u00e9, L. & Roth, S.) 63\u201379 (Springer, 2019).","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"677_CR46","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1038\/nature05453","volume":"445","author":"ES Lein","year":"2007","unstructured":"Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168\u2013176 (2007).","journal-title":"Nature"},{"key":"677_CR47","doi-asserted-by":"crossref","unstructured":"Ledig, C. et al. Photo-realistic single image super-resolution using a generative adversarial network. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 105\u2013114 (IEEE, 2017).","DOI":"10.1109\/CVPR.2017.19"},{"key":"677_CR48","doi-asserted-by":"crossref","unstructured":"Hor\u00e9, A. & Ziou, D. Image quality metrics: PSNR vs. SSIM. In Proc. 20th International Conference on Pattern Recognition 2366\u20132369 (IEEE, 2010).","DOI":"10.1109\/ICPR.2010.579"},{"key":"677_CR49","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1038\/s41592-021-01080-z","volume":"18","author":"L Fang","year":"2021","unstructured":"Fang, L. et al. Deep learning-based point-scanning super-resolution imaging. Nat. Methods 18, 406\u2013416 (2021).","journal-title":"Nat. Methods"},{"key":"677_CR50","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1038\/nmeth.4605","volume":"15","author":"S Culley","year":"2018","unstructured":"Culley, S. et al. Quantitative mapping and minimization of super-resolution optical imaging artifacts. Nat. Methods 15, 263\u2013266 (2018).","journal-title":"Nat. Methods"},{"key":"677_CR51","doi-asserted-by":"crossref","unstructured":"Aggarwal, C. C. Neural Networks and Deep Learning (Springer, 2018).","DOI":"10.1007\/978-3-319-94463-0"},{"key":"677_CR52","doi-asserted-by":"crossref","unstructured":"Yasaka, K. & Abe, O. Deep learning and artificial intelligence in radiology: current applications and future directions. PLoS Med. 15, e1002707 (2018).","DOI":"10.1371\/journal.pmed.1002707"},{"key":"677_CR53","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1109\/TMI.2020.2973595","volume":"39","author":"L Zhang","year":"2020","unstructured":"Zhang, L. et al. Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation. IEEE Trans. Med. Imag. 39, 2531\u20132540 (2020).","journal-title":"IEEE Trans. Med. Imag."},{"key":"677_CR54","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1002\/oms.1210110605","volume":"11","author":"R Large","year":"1976","unstructured":"Large, R. & Knof, H. A comparison of negative and positive ion mass spectrometry. Org. Mass Spectrom. 11, 582\u2013598 (1976).","journal-title":"Org. Mass Spectrom."},{"key":"677_CR55","doi-asserted-by":"crossref","first-page":"2835","DOI":"10.1021\/acs.analchem.1c04564","volume":"94","author":"BJ Tyler","year":"2022","unstructured":"Tyler, B. J. et al. Denoising of mass spectrometry images via inverse maximum signal factors analysis. Anal. Chem. 94, 2835\u20132843 (2022).","journal-title":"Anal. Chem."},{"key":"677_CR56","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1038\/s41592-019-0458-z","volume":"16","author":"C Belthangady","year":"2019","unstructured":"Belthangady, C. & Royer, L. A. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat. Methods 16, 1215\u20131225 (2019).","journal-title":"Nat. Methods"},{"key":"677_CR57","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1364\/OPTICA.6.000921","volume":"6","author":"G Barbastathis","year":"2019","unstructured":"Barbastathis, G., Ozcan, A. & Situ, G. On the use of deep learning for computational imaging. Optica 6, 921\u2013943 (2019).","journal-title":"Optica"},{"key":"677_CR58","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1038\/s41592-019-0403-1","volume":"16","author":"E Moen","year":"2019","unstructured":"Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods 16, 1233\u20131246 (2019).","journal-title":"Nat. Methods"},{"key":"677_CR59","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1038\/s41592-018-0239-0","volume":"16","author":"H Wang","year":"2019","unstructured":"Wang, H. et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods 16, 103\u2013110 (2019).","journal-title":"Nat. Methods"},{"key":"677_CR60","unstructured":"Arentz, G. et al. in Advances in Cancer Research Vol. 134 (eds. Drake, R. R. & McDonnell, L. A.) 27\u201366 (Academic, 2017)."},{"key":"677_CR61","doi-asserted-by":"crossref","first-page":"115480","DOI":"10.1016\/j.trac.2019.04.012","volume":"120","author":"D Wolrab","year":"2019","unstructured":"Wolrab, D., Jir\u00e1sko, R., Chocholou\u0161kov\u00e1, M., Peterka, O. & Hol\u010dapek, M. Oncolipidomics: mass spectrometric quantitation of lipids in cancer research. Trends Anal. Chem. 120, 115480 (2019).","journal-title":"Trends Anal. Chem."},{"key":"677_CR62","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/nmeth.4072","volume":"14","author":"A Palmer","year":"2017","unstructured":"Palmer, A. et al. FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry. Nat. Methods 14, 57\u201360 (2017).","journal-title":"Nat. Methods"},{"key":"677_CR63","doi-asserted-by":"crossref","first-page":"15295","DOI":"10.1021\/acs.analchem.1c02470","volume":"93","author":"MD Metodiev","year":"2021","unstructured":"Metodiev, M. D., Steven, R. T., Loizeau, X., Takats, Z. & Bunch, J. Modality agnostic model for spatial resolution in mass spectrometry imaging: application to MALDI MSI data. Anal. Chem. 93, 15295\u201315305 (2021).","journal-title":"Anal. Chem."},{"key":"677_CR64","doi-asserted-by":"crossref","first-page":"7302","DOI":"10.1021\/acs.analchem.6b01655","volume":"88","author":"F Zubair","year":"2016","unstructured":"Zubair, F., Prentice, B. M., Norris, J. L., Laibinis, P. E. & Caprioli, R. M. Standard reticle slide to objectively evaluate spatial resolution and instrument performance in imaging mass spectrometry. Anal. Chem. 88, 7302\u20137311 (2016).","journal-title":"Anal. Chem."},{"key":"677_CR65","doi-asserted-by":"crossref","unstructured":"Blau, Y., Mechrez, R., Timofte, R., Michaeli, T. & Zelnik-Manor, L. in Computer Vision \u2013 ECCV 2018 Workshops (eds Leal-Taix\u00e9, L. & Roth, S.) 334\u2013355 (Springer, 2019).","DOI":"10.1007\/978-3-030-11021-5_21"},{"key":"677_CR66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2016.12.009","volume":"158","author":"C Ma","year":"2017","unstructured":"Ma, C., Yang, C.-Y., Yang, X. & Yang, M.-H. Learning a no-reference quality metric for single-image super-resolution. Comput. Vis. Image Und. 158, 1\u201316 (2017).","journal-title":"Comput. Vis. Image Und."},{"key":"677_CR67","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","volume":"20","author":"A Mittal","year":"2013","unstructured":"Mittal, A., Soundararajan, R. & Bovik, A. C. Making a \u2018completely blind\u2019 image quality analyzer. IEEE Signal Process. Lett. 20, 209\u2013212 (2013).","journal-title":"IEEE Signal Process. Lett."},{"key":"677_CR68","doi-asserted-by":"crossref","first-page":"2719","DOI":"10.1021\/acs.analchem.8b04395","volume":"91","author":"X Wang","year":"2019","unstructured":"Wang, X., Hou, Y., Hou, Z., Xiong, W. & Huang, G. Mass spectrometry imaging of brain cholesterol and metabolites with trifluoroacetic acid-enhanced desorption electrospray ionization. Anal. Chem. 91, 2719\u20132726 (2019).","journal-title":"Anal. Chem."},{"key":"677_CR69","doi-asserted-by":"publisher","unstructured":"Liao, T. Optical data for MOSR. Figshare https:\/\/doi.org\/10.6084\/m9.figshare.22639936.v1 (2023).","DOI":"10.6084\/m9.figshare.22639936.v1"},{"key":"677_CR70","doi-asserted-by":"publisher","unstructured":"USTC-xlab. USTC-xlab\/MOSR: MOSR for MSI images (v2.0.1). Zenodo https:\/\/doi.org\/10.5281\/zenodo.7833505 (2023).","DOI":"10.5281\/zenodo.7833505"}],"container-title":["Nature Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42256-023-00677-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-023-00677-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-023-00677-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T14:35:52Z","timestamp":1702650952000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42256-023-00677-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,22]]},"references-count":70,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["677"],"URL":"https:\/\/doi.org\/10.1038\/s42256-023-00677-7","relation":{},"ISSN":["2522-5839"],"issn-type":[{"value":"2522-5839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,22]]},"assertion":[{"value":"2 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}