Publications by Ozan Öktem
Peer reviewed
Articles
[1]
S. Banert et al., "Accelerated Forward-Backward Optimization Using Deep Learning," SIAM Journal on Optimization, vol. 34, no. 2, pp. 1236-1263, 2024.
[2]
T. Buddenkotte et al., "Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation," Computers in Biology and Medicine, vol. 163, 2023.
[3]
T. Buddenkotte et al., "Deep learning-based segmentation of multisite disease in ovarian cancer," EUROPEAN RADIOLOGY EXPERIMENTAL, vol. 7, no. 1, 2023.
[4]
L. E. Sanchez et al., "Integrating Artificial Intelligence Tools in the Clinical Research Setting : The Ovarian Cancer Use Case," Diagnostics, vol. 13, no. 17, 2023.
[5]
S. Mukherjee et al., "Learned Reconstruction Methods With Convergence Guarantees : A survey of concepts and applications," IEEE signal processing magazine (Print), vol. 40, no. 1, pp. 164-182, 2023.
[6]
W. Diepeveen et al., "Regularizing Orientation Estimation in Cryogenic Electron Microscopy Three-Dimensional Map Refinement through Measure-Based Lifting over Riemannian Manifolds," SIAM Journal on Imaging Sciences, vol. 16, no. 3, pp. 1440-1490, 2023.
[7]
C. Esteve-Yague et al., "Spectral decomposition of atomic structures in heterogeneous cryo-EM," Inverse Problems, vol. 39, no. 3, pp. 034003, 2023.
[8]
J. Rudzusika, T. Koehler and O. Öktem, "Deep Learning-Based Dictionary Learning and Tomographic Image Reconstruction," SIAM Journal on Imaging Sciences, vol. 15, no. 4, pp. 1729-1764, 2022.
[9]
H. Andrade-Loarca et al., "Deep microlocal reconstruction for limited-angle tomography," Applied and Computational Harmonic Analysis, vol. 59, pp. 155-197, 2022.
[10]
G. Zickert, O. Öktem and C. E. Yarman, "Joint Gaussian dictionary learning and tomographic reconstruction," Inverse Problems, vol. 38, no. 10, 2022.
[11]
E. Ström et al., "Photon-Counting CT Reconstruction With a Learned Forward Operator," IEEE Transactions on Computational Imaging, vol. 8, pp. 536-550, 2022.
[12]
J. Adler et al., "Task adapted reconstruction for inverse problems," Inverse Problems, vol. 38, no. 7, 2022.
[13]
C. Chen et al., "An efficient algorithm to compute the X-ray transform," International Journal of Computer Mathematics, 2021.
[14]
D. Kimanius et al., "Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination," IUCrJ, vol. 8, pp. 60-75, 2021.
[15]
A. Aspri et al., "A Data-Driven Iteratively Regularized Landweber Iteration," Numerical Functional Analysis and Optimization, 2020.
[16]
S. Banert et al., "Data-driven nonsmooth optimization," SIAM Journal on Optimization, vol. 30, no. 1, pp. 102-131, 2020.
[17]
B. Gris, C. Chen and O. Öktem, "Image reconstruction through metamorphosis," Inverse Problems, vol. 36, no. 2, 2020.
[18]
A. Hauptmann et al., "Multi-Scale Learned Iterative Reconstruction," IEEE Transactions on Computational Imaging, vol. 6, pp. 843-856, 2020.
[19]
H. Andrade-Loarca, G. Kutyniok and O. Öktem, "Shearlets as feature extractor for semantic edge detection : the model-based and data-driven realm," Proceedings of the Royal Society. Mathematical, Physical and Engineering Sciences, vol. 476, no. 2243, 2020.
[20]
C. Chen, B. Gris and O. Öktem, "A new variational model for joint image reconstruction and motion estimation in spatiotemporal imaging," SIAM Journal on Imaging Sciences, vol. 12, no. 4, pp. 1686-1719, 2019.
[21]
H. Andrade-Loarca et al., "Extraction of digital wavefront sets using applied harmonic analysis and deep neural networks," SIAM Journal on Imaging Sciences, vol. 12, no. 4, pp. 1936-1966, 2019.
[22]
M. Siadat et al., "Joint Image Deconvolution and Separation Using Mixed Dictionaries," IEEE Transactions on Image Processing, vol. 28, no. 8, pp. 3936-3945, 2019.
[23]
S. Arridge et al., "Solving inverse problems using data-driven models," Acta Numerica, vol. 28, pp. 1-174, 2019.
[24]
J. Bergstrand et al., "Super-resolution microscopy can identify specific protein distribution patterns in platelets incubated with cancer cells," Nanoscale, vol. 11, no. 20, pp. 10023-10033, 2019.
[25]
L. F. Lang et al., "Template-Based Image Reconstruction from Sparse Tomographic Data," Applied mathematics and optimization, 2019.
[26]
C. Chen and O. Öktem, "Indirect image registration with large diffeomorphic deformations," SIAM Journal on Imaging Sciences, vol. 11, no. 1, pp. 575-617, 2018.
[27]
J. Adler and O. Öktem, "Learned Primal-Dual Reconstruction," IEEE Transactions on Medical Imaging, vol. 37, no. 6, pp. 1322-1332, 2018.
[28]
M. Siadat, N. Aghazadeh and O. Öktem, "Reordering for improving global Arnoldi-Tikhonov method in image restoration problems," Signal, Image and Video Processing, vol. 12, no. 3, pp. 497-504, 2018.
[29]
A. H. Tavabi et al., "Tunable Ampere phase plate for low dose imaging of biomolecular complexes," Scientific Reports, vol. 8, 2018.
[30]
M. Reuss et al., "Measuring true localization accuracy in super resolution microscopy with DNA-origami nanostructures," New Journal of Physics, vol. 19, no. 2, 2017.
[31]
O. Öktem et al., "Shape-based image reconstruction using linearized deformations," Inverse Problems, vol. 33, no. 3, 2017.
[32]
J. Adler and O. Öktem, "Solving ill-posed inverse problems using iterative deep neural networks," Inverse Problems, vol. 33, no. 12, 2017.
[33]
S. Hahn et al., "Spectral transfer from phase to intensity in Fresnel diffraction," PHYSICAL REVIEW A, vol. 93, no. 5, 2016.
[34]
M. Vulovic et al., "Image formation modeling in cryo-electron microscopy," Journal of Structural Biology, vol. 183, no. 1, pp. 19-32, 2013.
[35]
A. Gopinath et al., "Shape-based regularization of electron tomographic reconstruction," IEEE Transactions on Medical Imaging, vol. 31, no. 12, pp. 2241-2252, 2012.
[36]
H. Rullgard et al., "Simulation of transmission electron microscope images of biological specimens," Journal of Microscopy, vol. 243, no. 3, pp. 234-256, 2011.
[37]
O. Öktem, E. T. Quinto and U. Skoglund, "Electron Lambda-tomography," Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 51, pp. 21842-21847, 2009.
[38]
L. Norlén, O. Öktem and U. Skoglund, "Molecular cryo-electron tomography of vitreous tissue sections : current challenges," Journal of Microscopy, vol. 235, no. 3, pp. 293-307, 2009.
[39]
O. Öktem and D. Fanelli, "Electron tomography : A short overview with an emphasis on the absorption potential model for the forward problem," Inverse Problems, vol. 24, no. 1, pp. 013001, 2008.
[40]
O. Öktem and E. T. Quinto, "Local tomography in electron microscopy," SIAM Journal on Applied Mathematics, vol. 68, no. 5, pp. 1282-1303, 2008.
[41]
O. Öktem, H. Rullgård and U. Skoglund, "A component-wise iterated relative entropy regularization method with updated prior and regularization parameter," Inverse Problems, vol. 23, no. 5, pp. 2121-2139, 2007.
[42]
O. Öktem and E. T. Quinto, "Inversion of the X-ray transform from limited angle parallel beam region of interest data with applications to electron tomography," Proceedings in Applied Mathematics and Mechanics : PAMM, vol. 7, no. 1, pp. 1050301-1050302, 2007.
Conference papers
[43]
S. Mukherjee et al., "DATA-DRIVEN CONVEX REGULARIZERS FOR INVERSE PROBLEMS," in 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings, 2024, pp. 13386-13390.
[44]
A. Eguizabal, O. Öktem and M. Persson, "A deep learning one-step solution to material image reconstruction in photon counting spectral CT," in Proceedings Volume 12031, Medical Imaging 2022: Physics of Medical Imaging, 2022.
[45]
S. Mukherjee, O. Öktem and C. -. Schönlieb, "Adversarially Learned Iterative Reconstruction for Imaging Inverse Problems," in 8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021, 2021, pp. 540-552.
[46]
S. Mukherjee et al., "End-to-end reconstruction meets data-driven regularization for inverse problems," in Advances in Neural Information Processing Systems, 2021, pp. 21413-21425.
[47]
O. Öktem, C. Pouchol and O. Verdier, "Spatiotemporal PET Reconstruction Using ML-EM with Learned Diffeomorphic Deformation," in 2nd International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2019 held in Conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, 2019, pp. 151-162.
[48]
S. Lunz, O. Öktem and C. -. Schönlieb, "Adversarial regularizers in inverse problems," in Advances in Neural Information Processing Systems, 2018, pp. 8507-8516.
[49]
G. Dong et al., "Infinite dimensional optimization models and PDEs for dejittering," in 5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015, 2015, pp. 678-689.
Chapters in books
[50]
A. Ringh et al., "High-level algorithm prototyping : An example extending the TVR-DART algorithm," in Discrete Geometry for Computer Imagery : 20th IAPR International Conference, DGCI 2017, Vienna, Austria, September 19 – 21, 2017, Proceedings, : Springer, 2017, pp. 109-121.
[51]
O. Öktem, "Reconstruction methods in electron tomography," in Mathematical Methods in Biomedical Imaging and Intensity-Modulated Radiation Therapy (IMRT), Y. Censor, Jiang M., and Louis A. K. Ed., : Springer Berlin/Heidelberg, 2008, pp. 289-320.
Non-peer reviewed
Articles
[52]
E. T. Quinto, Ö. Ozan and U. Skoglund, "Reply to Wang and Yu : Both electron lambda tomography and interior tomography have their uses," Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 22, pp. E94-E95, 2010.
Conference papers
[53]
A. Eguizabal, M. Persson and O. Öktem, "Learned Material Decomposition for Photon Counting CT," in Proceedings of the 16th Virtual International Meeting onFully 3D Image Reconstruction inRadiology and Nuclear Medicine, 2021, pp. 15-19.
Chapters in books
[54]
O. Öktem, "Mathematics of electron tomography," in Handbook of Mathematical Methods in Imaging: Volume 1, Second Edition, : Springer, 2015, pp. 937-1031.
[55]
L. Norlén, J. Anwar and O. Öktem, "Accessing the molecular organization of the stratum corneum using high-resolution electron microscopy and computer simulation," in Computational Biophysics of the Skin, : Pan Stanford Publishing, 2014, pp. 289-330.
Other
[56]
[57]
[58]
[59]
J. Bergstrand et al., "Super-resolution microscopy can identify specific protein distribution patterns in platelets incubated with cancer cells," (Manuscript).
[60]
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2024-08-11 03:28:41