Kristoffer Lindskov Hansen
Member of:
Radiology
1 - 3 out of 3Page size: 10
- 2023
- Published
Characterizing incidental mass lesions in abdominal dual-energy CT compared to conventional contrast-enhanced CT
Xu, J. J., Ulriksen, Peter Sommer, Bjerrum, C. W., Achiam, Michael Patrick, Resch, Timothy , Lönn, Lars & Hansen, Kristoffer Lindskov, 2023, In: Acta Radiologica. 64, 3, p. 945–950Research output: Contribution to journal › Journal article › Research › peer-review
- Published
Evaluation of thin-slice abdominal DECT using deep-learning image reconstruction in 74 keV virtual monoenergetic images: an image quality comparison
Xu, J. J., Lönn, Lars, Budtz-Joergensen, Esben, Jawad, S., Ulriksen, P. S. & Hansen, Kristoffer Lindskov, 2023, In: Abdominal Radiology. 48, p. 1536-1544 9 p.Research output: Contribution to journal › Journal article › Research › peer-review
- Published
Performance and Agreement When Annotating Chest X-ray Text Reports - A Preliminary Step in the Development of a Deep Learning-Based Prioritization and Detection System
Li, D., Pehrson, Lea Marie, Bonnevie, R., Fraccaro, M., Thrane, J., Tøttrup, L., Lauridsen, C. A., Balaganeshan, Sedrah Butt, Jankovic, J., Andersen, T. T., Mayar, A., Hansen, Kristoffer Lindskov, Carlsen, Jonathan Frederik, Darkner, Sune & Nielsen, Michael Bachmann, 2023, In: Diagnostics. 13, 6, 24 p.Research output: Contribution to journal › Journal article › Research › peer-review
ID: 169426984
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Data on the quantitative assessment pulmonary ground-glass opacification from coronary computed tomography angiography datasets
Research output: Contribution to journal › Journal article › Research › peer-review
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131
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Evaluation of Peak Reflux Velocities with Vector Flow Imaging and Spectral Doppler Ultrasound in Varicose Veins
Research output: Contribution to journal › Journal article › Research › peer-review
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103
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The performance of deep learning algorithms on automatic pulmonary nodule detection and classification tested on different datasets that are not derived from LIDC-IDRI: A systematic review
Research output: Contribution to journal › Review › Research › peer-review
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