- Published
High-school dropout prediction using machine learning: a Danish large-scale study
Şara, N., Halland, R., Igel, Christian & Alstrup, Stephen, 2015, Proceedings. ESANN 2015: 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen, M. (ed.). i6doc.com, p. 319-324 6 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
Crowdsourced emphysema assessment
Ørting, S. N., Cheplygina, V., Petersen, Jens, Thomsen, L. H., Wille, M. M. W. & de Bruijne, Marleen, 2017, Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis: 6th Joint International Workshops, CVII-STENT 2017 and Second International Workshop, LABELS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10–14, 2017, Proceedings. Cardoso, M. J., Arbel, T., Lee, S-L., Cheplygina, V., Balocco, S., Mateus, D., Zahnd, G., Maier-Hein, L., Dermirci, S., Granger, E., Duong, L., Carbonneau, M-A., Albarquoni, S. & Carneiro, G. (eds.). Springer, p. 126-135 10 p. (Lecture notes in computer science, Vol. 10552).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
Quantifying emphysema extent from weakly labeled CT scans of the lungs using label proportions learning
Ørting, S. N., Petersen, Jens, Wille, M., Thomsen, L. & de Bruijne, Marleen, 2016, The Sixth International Workshop on Pulmonary Image Analysis. Beichel, R. R., Farahani, K., Jacobs, C., Kabus, S., Kiraly, A. P., Kuhnigk, J-M., McClelland, J. R., Mori, K., Petersen, J. & S. R. (eds.). CreateSpace Independent Publishing Platform , p. 31-42 11 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
Popular Politics: Comparing Popular Stories across News Media in Election Seasons
Ørmen, Jacob & Petersen, C., 1 Jun 2017.Research output: Contribution to conference › Conference abstract for conference › Research
- Published
Sensitivity of screening mammography by density and texture: a cohort study from a population-based screening program in Denmark
von Euler-Chelpin, My Catarina, Lillholm, Martin, Vejborg, I., Nielsen, Mads & Lynge, Elsebeth, 2019, In: Breast Cancer Research. 21, 1Research output: Contribution to journal › Journal article › Research › peer-review
- Published
Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines
van Tulder, G. & de Bruijne, Marleen, 2016, In: IEEE Transactions on Medical Imaging. 35, 5, p. 1262-1272 11 p.Research output: Contribution to journal › Journal article › Research › peer-review
- Published
Learning features for tissue classification with the classification restricted Boltzmann machine
van Tulder, G. & de Bruijne, Marleen, 2014, Medical Computer Vision: Algorithms for Big Data: International Workshop, MCV 2014, Held in Conjunction with MICCAI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers. Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Zhang, S., Cai, W. T. & Metaxas, D. (eds.). Springer, p. 47-58 12 p. (Lecture notes in computer science).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
Why does synthesized data improve multi-sequence classification?
van Tulder, G. & de Bruijne, Marleen, 2015, Medical image computing and computer assisted interventions - MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I. Springer, p. 531-538 8 p. (Lecture notes in computer science, Vol. 9349).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
A transfer-learning approach to image segmentation across scanners by maximizing distribution similarity
van Opbroek, A., Ikram, M. A., Vernooij, M. W. & de Bruijne, Marleen, 2013, Machine Learning in Medical Imaging: 4th International Workshop, MLMI 2013, held in conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013. Proceedings. Wu, G., Zhang, D., Shen, D., Yan, P., Suzuki, K. & Wang, F. (eds.). Springer, p. 49-56 8 p. (Lecture notes in computer science, Vol. 8184).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
Transfer learning improves supervised image segmentation across imaging protocols
van Opbroek, A., Ikram, M. A., Vernooij, M. W. & de Bruijne, Marleen, 2015, In: IEEE Transactions on Medical Imaging. 34, 5, p. 1018-1030 13 p.Research output: Contribution to journal › Journal article › Research › peer-review
Most downloads
-
6947
downloads
Addressing the path-length-dependency confound in white matter tract segmentation
Research output: Contribution to journal › Journal article › Research › peer-review
Published -
6257
downloads
Virtual Trackballs Revisited
Research output: Contribution to journal › Journal article › Research › peer-review
Published -
5778
downloads
Locally orderless registration
Research output: Contribution to journal › Journal article › Research › peer-review
Published
Latest publications
Field report for Platform mBox: Designing an Open MMLA Platform
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Local Gamma Augmentation for Ischemic Stroke Lesion Segmentation on MRI
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data
Research output: Contribution to journal › Letter › Research › peer-review