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ORCIDMichiel Kallenberg
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Journal Articles
- 2016
[j2]Michiel Kallenberg
, Kersten Petersen, Mads Nielsen, Andrew Y. Ng, Pengfei Diao, Christian Igel, Celine M. Vachon, Katharina Holland, Rikke Rass Winkel, Nico Karssemeijer, Martin Lillholm:
Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring. IEEE Trans. Medical Imaging 35(5): 1322-1331 (2016)- 2015
- [j1]Albert Gubern-Mérida, Michiel Kallenberg, Ritse Mann, Robert Marti, Nico Karssemeijer:
Breast Segmentation and Density Estimation in Breast MRI: A Fully Automatic Framework. IEEE J. Biomed. Health Informatics 19(1): 349-357 (2015)
Conference and Workshop Papers
- 2019
- [c14]Doiriel Vanegas C., Mahlet A. Birhanu, Nico Karssemeijer, Albert Gubern-Mérida, Michiel Kallenberg:
A deep learning method for volumetric breast density estimation from processed full field digital mammograms. Medical Imaging: Computer-Aided Diagnosis 2019: 109500F - 2016
- [c13]Michiel Kallenberg, Mads Nielsen, Katharina Holland, Nico Karssemeijer, Christian Igel, Martin Lillholm:
Learning Density Independent Texture Features. Digital Mammography / IWDM 2016: 299-306 - 2014
- [c12]Katharina Holland, Michiel Kallenberg, Ritse Mann, Carla van Gils, Nico Karssemeijer:
Stability of Volumetric Tissue Composition Measured in Serial Screening Mammograms. Digital Mammography / IWDM 2014: 239-244 - 2013
- [c11]Albert Gubern-Mérida, Lei Wang, Michiel Kallenberg, Robert Marti, Horst K. Hahn, Nico Karssemeijer:
Breast segmentation in MRI: quantitative evaluation of three methods. Medical Imaging: Image Processing 2013: 86693G - 2012
- [c10]Jelena Bozek, Michiel Kallenberg, Mislav Grgic, Nico Karssemeijer:
Comparison of Lesion Size Using Area and Volume in Full Field Digital Mammograms. Digital Mammography / IWDM 2012: 96-103 - [c9]Albert Gubern-Mérida, Michiel Kallenberg, Robert Marti, Nico Karssemeijer:
Segmentation of the Pectoral Muscle in Breast MRI Using Atlas-Based Approaches. MICCAI (2) 2012: 371-378 - 2011
- [c8]Albert Gubern-Mérida, Michiel Kallenberg, Robert Marti, Nico Karssemeijer:
Multi-class Probabilistic Atlas-Based Segmentation Method in Breast MRI. IbPRIA 2011: 660-667 - [c7]Jan M. Lesniak, Rianne Hupse, Michiel Kallenberg, Maurice Samulski, Rémi Blanc, Nico Karssemeijer, Gábor Székely:
Computer aided detection of breast masses in mammography using support vector machine classification. Medical Imaging: Computer-Aided Diagnosis 2011: 79631K - [c6]Michiel G. J. Kallenberg, Mariëtte A. J. Lokate, Carla H. van Gils, Nico Karssemeijer:
Automatic breast density segmentation based on pixel classification. Medical Imaging: Computer-Aided Diagnosis 2011: 796307 - 2010
- [c5]Michiel Kallenberg, Nico Karssemeijer:
Comparison of Tilt Correction Methods in Full Field Digital Mammograms. Digital Mammography / IWDM 2010: 191-196 - 2009
- [c4]Michiel Kallenberg, Nico Karssemeijer:
Using volumetric density estimation in computer aided mass detection in mammography. Medical Imaging: Computer-Aided Diagnosis 2009: 72600T - 2008
- [c3]Nico Karssemeijer, Andrea Hupse, Maurice Samulski, Michiel Kallenberg, Carla Boetes, Gerard J. den Heeten:
An Interactive Computer Aided Decision Support System for Detection of Masses in Mammograms. Digital Mammography / IWDM 2008: 273-278 - [c2]Michiel Kallenberg, Nico Karssemeijer:
The Effect of Training Sample Size on Performance of Mass Detection. Digital Mammography / IWDM 2008: 343-349 - [c1]Michiel Kallenberg, Nico Karssemeijer:
The effect of training with SFM images in a FFDM CAD system. Medical Imaging: Computer-Aided Diagnosis 2008: 69151O
Parts in Books or Collections
- 2017
- [p1]Akshay Pai, Yuan-Ching Teng, Joseph Blair, Michiel Kallenberg, Erik B. Dam, Stefan Sommer, Christian Igel, Mads Nielsen:
Characterization of Errors in Deep Learning-Based Brain MRI Segmentation. Deep Learning for Medical Image Analysis 2017: 223-242
Informal and Other Publications
- 2023
- [i1]Michiel G. J. Kallenberg, Bernardo Maestrini, Ron van Bree, Paul Ravensbergen, Christos Pylianidis, Frits van Evert, Ioannis N. Athanasiadis:
Integrating processed-based models and machine learning for crop yield prediction. CoRR abs/2307.13466 (2023)
Coauthor Index
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