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Schweidtmann, A.M., Rittig, J.G., Weber, J.M., Grohe, M., Dahmen, M. (2023). Physical pooling functions in graph neural networks for molecular property prediction. Computers & Chemical Engineering. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:WqliGbK-hY8C ] Begall, M.J., Schweidtmann, A.M., Mhamdi A., Mitsos, A. (2023). Geometry optimization of a continuous millireactor via CFD and Bayesian optimization. Computers & Chemical Engineering. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:JQOojiI6XY0C ] Theisen, M.F., Flores, K.N., Balhorn, L.S., Schweidtmann, A.M. (2023). Digitization of chemical process flow diagrams using deep convolutional neural networks. Digital Chemical Engineering. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:dQ2og3OwTAUC ] Vogel, G., Balhorn, L.S., Schweidtmann, A.M. (2023). Learning from flowsheets: A generative transformer model for autocompletion of flowsheets. Computers & Chemical Engineering. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:LjlpjdlvIbIC ] Rittig, J.G., Hicham, K.B., Schweidtmann, A.M., Dahmen, M., Mitsos, A. (2023). Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids. Computers & Chemical Engineering. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:SdhP9T11ey4C ] Balhorn, L.S., Hirtreiter, E., Luderer, L., Schweidtmann, A.M. (2023). Data augmentation for machine learning of chemical process flowsheets. arXiv preprint arXiv. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:W5xh706n7nkC ] Gao, Q., Yang, H., Shanbhag, S.M., Schweidtmann, A.M. (2023). Transfer learning for process design with reinforcement learning. arXiv preprint arXiv. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:_Ybze24A_UAC ] Schulze Balhorn, L., Hirtreiter, E., Luderer, L., Schweidtmann, A.M. (2023). Data augmentation for machine learning of chemical process flowsheets. arXiv e-prints, arXiv. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:Fu2w8maKXqMC ] Fleitmann, L., Ackermann, P., Schilling, J., Kleinekorte, J., Rittig, J.G. (2023). Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning. Energy & Fuels. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:uLbwQdceFCQC ] Stops, L., Leenhouts, R., Gao, Q., Schweidtmann, A.M. (2023). Flowsheet generation through hierarchical reinforcement learning and graph neural networks. AIChE Journal. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:PR6Y55bgFSsC ] Ferreira, V.M., Plein, S., Wong, T.C., Tao, Q., Raisi-Estabragh, Z., Jain, S.S., Han, Y. (2023). Cardiovascular magnetic resonance for evaluation of cardiac involvement in COVID-19: recommendations by the Society for Cardiovascular Magnetic Resonance. Journal of Cardiovascular Magnetic Resonance. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:JWITY9-sCbMC ] Chen, Y., Staring, M., Wolterink, J.M., Tao, Q. (2023). Local implicit neural representations for multi-sequence MRI translation. arXiv preprint arXiv. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:dAp6zn-oMfAC ] Sramko, M., Abdel-Kafi, S., Wijnmaalen, A.P., Tao, Q., van der Geest, R.J. (2023). Head-to-Head Comparison of T1 Mapping and Electroanatomical Voltage Mapping in Patients With Ventricular Arrhythmias. JACC: Clinical Electrophysiology. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:zUl2_INMlC4C ] Glashan, C., Blom, S.A., Kimura, Y., Tao, Q., Jongbloed, M.R.M., Zeppenfeld, K. (2023). Integration of electroanatomical mapping data with whole human heart histology to identify the morphological characteristics of VT substrate in patients with NICM. Europace. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:OzeSX8-yOCQC ] Coletti, C., Fotaki, A., Tourais, J., Zhao, Y., van de Steeg?Henzen, C. (2023). Robust cardiac T 1? mapping at 3T using adiabatic spin?lock preparations. Magnetic Resonance in Medicine. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:wLxue7F8ec0C ] Glashan, C.A., Blom, S., Tao, Q., Jongbloed, M.R., Zeppenfeld, K. (2023). PO-05-032 Integration of electroanatomical mapping data with whole human heart histology to identify the morphological characteristics of vt substrate in patients with nicm. Heart Rhythm. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=100&pagesize=100&citation_for_view=djCHmzsAAAAJ:QVtou7C4vgoC ] Lin, L., Zhou, X.H., Zheng, M., Xie, Q.X., Tao, Q., Lamb, H.J. (2023). Myocardial extracellular volume fraction quantification based on T1 mapping at 3 T: quality optimization by contour-based registration and segmental analysis. Acta Radiologica. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=100&pagesize=100&citation_for_view=djCHmzsAAAAJ:RuPIJ_LgqDgC ] Hirtreiter, E., Balhorn, L.S., Schweidtmann, A.M. (2022). Towards automatic generation of Piping and Instrumentation Diagrams (P&IDs) with Artificial Intelligence. arXiv preprint arXiv. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:hkOj_22Ku90C ] Bongartz, D., Fahr, S., Najman, J., Kappatou, C.D., Sass, S., Schweidtmann, A.M. (2022). MAiNGO–A Global Optimizer for Process Engineering: Algorithm and Applications in Process Design and Machine Learning. Chemie Ingenieur Technik. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:N5tVd3kTz84C ] Schweidtmann, A.M., Bongartz, D., Mitsos, A. (2022). Optimization with Trained Machine Learning Models Embedded. arXiv preprint arXiv. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:eq2jaN3J8jMC ] Rittig, J.G., Gao, Q., Dahmen, M., Mitsos, A., Schweidtmann, A.M. (2022). Graph neural networks for the prediction of molecular structure-property relationships. arXiv preprint arXiv. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:VL0QpB8kHFEC ] Vogel, G., Balhorn L.S., Hirtreiter E., Schweidtmann, A.M. (2022). SFILES 2.0: An extended text-based flowsheet representation. arXiv preprint arXiv. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:ye4kPcJQO24C ] Stops, L., Leenhouts, R., Gao, Q., Schweidtmann, A.M. (2022). Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks. arXiv preprint arXiv. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:5awf1xo2G04C ] Schweidtmann, A.M., Weber, J.M., Wende, C., Netze, L., Mitsos, A. (2022). Obey validity limits of data-driven models through topological data analysis and one-class classification. Optimization and Engineering. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:1qzjygNMrQYC ] Rittig, J.G., Ritzert, M., Schweidtmann, A.M., Winkler, S., Weber, J.M., Morsch, P. (2022). Graph machine learning for design of high?octane fuels. AIChE Journal. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:JoZmwDi-zQgC ] Schweidtmann, A. M., Esche, E., Fischer, A., Kloft, M., Repke, J. U., Sager, S., & Mitsos, A. (2021). Machine learning in chemical engineering: a perspective. Chemie Ingenieur Technik. [https://onlinelibrary.wiley.com/doi/10.1002/cite.202100083 ] Liebal, U. W., Köbbing, S., Netze, L., Schweidtmann, A. M., Mitsos, A., & Blank, L. M. (2021). Insight to Gene Expression from Promoter Libraries with the Machine Learning Workflow Exp2Ipynb. Frontiers in Bioinformatics. [https://www.frontiersin.org/articles/10.3389/fbinf.2021.747428/full ] Merkelbach, K., Schweidtmann, A.M., Müller, Y., Schwoebel, P., Mhamdi, A. (2022). HybridML: Open source platform for hybrid modeling. Computers & Chemical Engineering. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:_B80troHkn4C ] Stocker, M., Heger, T., Schweidtmann, A.M., ?wiek-Kupczy?ska, H., Penev, L. (2022). SKG4EOSC-Scholarly Knowledge Graphs for EOSC: Establishing a backbone of knowledge graphs for FAIR Scholarly Information in EOSC. Research Ideas and Outcomes. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:PELIpwtuRlgC ] Jorayev, P., Russo, D., Tibbetts, J.D., Schweidtmann, A.M., Deutsch, P., Bull, S.D. (2022). Multi-objective Bayesian optimisation of a two-step synthesis of p-cymene from crude sulphate turpentine. Chemical Engineering Science. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:bnK-pcrLprsC ] Balhorn, L.S., Gao, Q., Goldstein, D., Schweidtmann, A.M. (2022). Flowsheet recognition using deep convolutional neural networks. Computer Aided Chemical Engineering. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:NJ774b8OgUMC ] Petersen, S.E., Friedrich, M.G., Leiner, T., Elias, M.D., Ferreira, V.M., Fenski, M. (2022). Cardiovascular magnetic resonance for patients with COVID-19. JACC: Cardiovascular Imaging. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&citation_for_view=djCHmzsAAAAJ:8dzOF9BpDQoC ] Zhou, Z., Zu, X., Wang, Y., Lelieveldt, B.P.F., Tao, Q. (2022). Deep Recursive Embedding for High-Dimensional Data. IEEE Transactions on Visualization and Computer Graphics. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:mel-f30kHHgC ] Neve, O.M., Chen, Y., Tao, Q., Romeijn, S.R., de Boer, N.P., Grootjans, W. (2022). Fully Automated 3D Vestibular Schwannoma Segmentation with and without Gadolinium-based Contrast Material: A Multicenter, Multivendor Study. Radiology: Artificial Intelligence. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:nPT8s1NX_-sC ] Zu, X., Tao, Q. (2022). SpaceMAP: Visualizing High-dimensional Data by Space Expansion. International Conference on Machine Learning. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:s85pQhAUCrAC ] Glashan, C.A., Tofig, B.J., Beukers, H., Tao, Q., Blom, S.A., Villadsen, P.R. (2022). Multielectrode unipolar voltage mapping and electrogram morphology to identify post-infarct scar geometry: validation by histology. JACC: Clinical Electrophysiology. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:RtRctb2lSbAC ] Huang, L., Tao, Q., Zhao, P., Ji, S., Jiang, J, van der Geest, R.J., Xia, L. (2022). Using multi-parametric quantitative MRI to screen for cardiac involvement in patients with idiopathic inflammatory myopathy. Scientific Reports. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:oPLKW5k6eA4C ] Lin, L., Dekkers, I.A., Tao, Q., Paiman, E.H.M., Bizino, M.B., Jazet, I.M., Lamb, H.J. (2022). Effect of glucose regulation on renal parenchyma and sinus fat volume in patients with type 2-diabetes. Diabetes & metabolism. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=100&pagesize=100&citation_for_view=djCHmzsAAAAJ:37UQlXuwjP4C ] Omara, S., Glashan, C.A., Tofig, B.J., Tao, Q., Blom, S.A., Nielsen, J.C., Lukac, P. (2022). Assessing the field of view of multisize electrodes in ischemic cardiomyopathy by validating against ex-vivo high resolution cardiac magnetic resonance. Europace. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=100&pagesize=100&citation_for_view=djCHmzsAAAAJ:LGlY6t8CeOMC ] Tao, Q., van der Geest, R.J. (2022). Artificial Intelligence-Based Evaluation of Functional Cardiac Magnetic Resonance Imaging. Artificial Intelligence in Cardiothoracic Imaging. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=100&pagesize=100&citation_for_view=djCHmzsAAAAJ:hGdtkIFZdKAC ] Shi, R., Chen, B., Wu, C., Wesemann, L., Hu, J., Xu, J., Zhou, Y., Tao, Q., Wu, L. (2022). Left ventricular thrombus after acute ST-segment elevation myocardial infarction: multi-parametric cardiac magnetic resonance imaging with long-term outcomes. The International Journal of Cardiovascular Imaging. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=100&pagesize=100&citation_for_view=djCHmzsAAAAJ:TaaCk18tZOkC ] Jose, N.A., Kovalev, M., Bradford, E., Schweidtmann, A.M., Zeng, H.C., Lapkin, A.A. (2021). Pushing nanomaterials up to the kilogram scale–An accelerated approach for synthesizing antimicrobial ZnO with high shear reactors, machine learning and high-throughput analysis. Chemical Engineering Journal. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:XiVPGOgt02cC ] Schweidtmann, A.M., Esche, E., Fischer, A., Kloft, M., Repke, J.U., Sager, S. (2021). Machine learning in chemical engineering: A perspective. Chemie Ingenieur Technik. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:AXPGKjj_ei8C ] Liebal, U.W., Köbbing, S., Netze, L., Schweidtmann, A.M., Mitsos, A., Blank, L.M. (2021). Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb. Frontiers in Bioinformatics. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&pagesize=80&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:Mojj43d5GZwC ] Weber, J.M., Guo, Z., Zhang, C., Schweidtmann, A.M., Lapkin, A.A. (2021). Chemical data intelligence for sustainable chemistry. Chemical Society Reviews. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&pagesize=80&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:HE397vMXCloC ] Xiong, Z., Xia, Q., Hu, Z., Huang, N., Bian, C., Zheng, Y., Vesal, S., Ravikumar, N. (2021). A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Medical image analysis. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&citation_for_view=djCHmzsAAAAJ:ghEM2AJqZyQC ] Glashan, C.A., Beukers, H.K.C., Tofig, B.J., Tao, Q., Blom, S., Mertens, B. (2021). Mini-, micro-, and conventional electrodes: an in vivo electrophysiology and ex vivo histology head-to-head comparison. JACC: Clinical Electrophysiology. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:EsEWqaRxkBgC ] Lin, L., Dekkers, I.A., Huang, L., Tao, Q., Paiman, E.H.M., Bizino, M.B., Jazet, I.M. (2021). Renal sinus fat volume in type 2-diabetes mellitus is associated with glycated hemoglobin and metabolic risk factors. Journal of Diabetes and its Complications. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:JP7YXuLIOvAC ] Lin, L., Xie, Q., Zheng, M., Zhou, X., Dekkers, I.A., Tao, Q., Lamb, H.J. (2021). Identification of cardiovascular abnormalities by multiparametric magnetic resonance imaging in end-stage renal disease patients with preserved left ventricular ejection fraction. European Radiology. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:CNPyR2KL9-0C ] Glashan, C.A., Beukers, H.K.C., Tofig, B.J., Tao, Q., Blom, S., Mertens, B. (2021). Mini-, Micro-, and Conventional Electrodes. JACC: Clinical Electrophysiology. [https://pubmed.ncbi.nlm.nih.gov/33602400/ ] Yan, W., Huang, L., Xia, L., Gu, S., Yan, F., Wang, Y., Tao, Q. (2020). MRI manufacturer shift and adaptation: Increasing the generalizability of deep learning segmentation for MR images acquired with different scanners. Radiology: Artificial Intelligence. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:-6RzNnnwWf8C ] Tao, Q., Lelieveldt, B.P.F., van der Geest, R.J. (2020). Deep learning for quantitative cardiac MRI. American Journal of Roentgenology. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:CYCckWUYoCcC ] Lin, L., Dekkers, I.A., Tao, Q., Lamb, H.J. (2020). Novel artificial neural network and linear regression based equation for estimating visceral adipose tissue volume. Clinical Nutrition. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:-DxkuPiZhfEC ] Qiao, M., Wang, Y., Guo, Y., Huang, L., Xia, L., Tao, Q. (2020). Temporally coherent cardiac motion tracking from cine MRI: Traditional registration method and modern CNN method. Medical Physics. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:T_ojBgVMvoEC ] Noortman, W.A., Vriens, D., Grootjans, W., Tao, Q., de Geus-Oei, L.F. (2020). Nuclear medicine radiomics in precision medicine: Why we can’t do without artificial intelligence. QJ Nucl. Med. Mol. Imaging. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:D_tqNUsBuKoC ] Venlet, J., Tao, Q., de Graaf, M.A., Glashan, C.A., de Riva Silva, M. (2020). RV tissue heterogeneity on CT: a novel tool to identify the VT substrate in ARVC. JACC: Clinical Electrophysiology. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:1tZ8xJnm2c8C ] Xie, X., Wen, D., Zhang, R., Tao, Q., Wang, C., Xie, S., Liu, H., Zheng, M. (2020). Pressure-flow curve derived from coronary CT angiography for detection of significant hemodynamic stenosis. European Radiology. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:wE-fMHVdjMkC ] Glashan, C.A., Tofig, B.J., Tao, Q., Blom, S.A., Sørensen, J.C.H., Zeppenfeld, K. (2020). Whole Heart Histology: A Method for the Direct Integration of Histology With Electrophysiological and Imaging Data. JACC: Clinical Electrophysiology. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:zdjWy_NXXwUC ] De Roos, A., Tao, Q. (2020). The Challenge of Automated Analysis of Myocardial Perfusion MRI: Is It Ready for Prime Time? Journal of Magnetic Resonance Imaging. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=100&pagesize=100&citation_for_view=djCHmzsAAAAJ:X9ykpCP0fEIC ] Venlet, J., Tao, Q., de Graaf, M.A., Glashan, C.A., de Riva Silva, M. (2020). RV Tissue Heterogeneity on CT. JACC: Clinical Electrophysiology. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=100&pagesize=100&citation_for_view=djCHmzsAAAAJ:YB4bud6kWLwC ]
Conferences
Ackermann, P., Fleitmann, L.H.J., Schilling, J., Kleinekorte, J., Rittig, J.G., Lehn, F. (2022). Molecular Design of Spark-Ignition Engine Fuels for Maximal Engine Efficiency. 10th International Conference “Fuel Science-From Production to Propulsion”. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:9vf0nzSNQJEC ] Zhao, Y., Yang, C., Schweidtmann, A.M., Tao, Q. (2022). Efficient Bayesian Uncertainty Estimation for nnU-Net. Medical Image Computing and Computer Assisted Intervention–MICCAI 2022. [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:ZuybSZzF8UAC ] Fleitmann, L.H.J., Ackermann, P., Schilling, J., Kleinekorte, J., Rittig, J.G. (2022). Molecular design of spark-ignition fuels by combining predictive thermodynamics and machine learning. 32nd European Symposium on Computer-Aided Process Engineering (ESCAPE-32). [https://scholar.google.de/citations?view_op=view_citation&hl=de&user=g-GwouoAAAAJ&sortby=pubdate&citation_for_view=g-GwouoAAAAJ:dTyEYWd-f8wC ] Tourais, J., Demirel, O.B., Tao, Q., Pierce, I., Thornton, G.D., Treibel, T.A. (2022). Myocardial Approximate Spin-lock Dispersion Mapping using a Simultaneous and Mapping at 3T MRI. Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=100&pagesize=100&citation_for_view=djCHmzsAAAAJ:X0DADzN9RKwC ] Yang, C., Zhao, Y., Huang, L., Xia, L., Tao, Q. (2022). DisQ: Disentangling Quantitative MRI Mapping of the Heart. International Conference on Medical Image Computing and Computer-Assisted …. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=100&pagesize=100&citation_for_view=djCHmzsAAAAJ:nqdriD65xNoC ] Zhou Z, Zu X, Wang Y, Lelieveldt BPF, Tao Q. (2021). Deep Recursive Embedding for High-Dimensional Data. IEEE Transactions on Visualization and Computer Graphics. [https://ieeexplore.ieee.org/document/9585419 ] Zhao, Y., Yang, C., Schweidtmann, A., Tao, Q. (2020). Efficient Bayesian Uncertainty Estimation for nnU-Nethttps://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:z8nqeaKD1nsC. International Conference on Medical Image Computing and Computer-Assisted Intervention. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=djCHmzsAAAAJ&cstart=20&pagesize=80&citation_for_view=djCHmzsAAAAJ:z8nqeaKD1nsC ]
