Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Image segmentation are her primary areas of study. Her Artificial intelligence study frequently links to adjacent areas such as Machine learning. Her work on Active appearance model and Shape analysis as part of her general Computer vision study is frequently connected to Intensity, thereby bridging the divide between different branches of science.
The concepts of her Pattern recognition study are interwoven with issues in Contextual image classification and Histogram, Local binary patterns. Her Segmentation research includes themes of Image processing, Image and Tomography. Her work is dedicated to discovering how Image segmentation, Active shape model are connected with Linear model and other disciplines.
Her primary scientific interests are in Artificial intelligence, Pattern recognition, Segmentation, Radiology and Computer vision. Her study in Deep learning, Image segmentation, Image, Classifier and Voxel falls under the purview of Artificial intelligence. Her Pattern recognition research is multidisciplinary, incorporating perspectives in Artificial neural network, Feature and Regression.
Her biological study deals with issues like Airway, which deal with fields such as Lung cancer screening and Chest ct. Her Radiology research includes elements of Lumen, Cystic fibrosis and Lung, Bronchiectasis. Her Computer vision study combines topics from a wide range of disciplines, such as Weighting and Atlas.
Artificial intelligence, Pattern recognition, Segmentation, Artificial neural network and Deep learning are her primary areas of study. Within one scientific family, Marleen de Bruijne focuses on topics pertaining to Regression under Artificial intelligence, and may sometimes address concerns connected to Intraclass correlation. Marleen de Bruijne combines subjects such as Margin, Voxel, Feature and Graph with her study of Pattern recognition.
Her Segmentation research incorporates elements of Semi-supervised learning, Radiology and Carotid arteries. Her Artificial neural network study also includes
Her primary areas of study are Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Image segmentation. Her work carried out in the field of Artificial intelligence brings together such families of science as Graph and Graph neural networks. Marleen de Bruijne has included themes like Feature, Artificial neural network, Encoder, Graph and Voxel in her Pattern recognition study.
Her Segmentation research incorporates elements of Semi-supervised learning, Image, Radiology, Neuroimaging and Convolutional neural network. In the subject of general Radiology, her work in Computed tomography, Tomography and Pulmonary function testing is often linked to DLCO, thereby combining diverse domains of study. Marleen de Bruijne works mostly in the field of Deep learning, limiting it down to topics relating to Margin and, in certain cases, Regression, Region of interest, Random forest and Lung.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge
K. Murphy;B. van Ginneken;J. M. Reinhardt;S. Kabus.
IEEE Transactions on Medical Imaging (2011)
Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Veronika Cheplygina;Marleen de Bruijne;Josien P.W. Pluim.
Medical Image Analysis (2019)
Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns
Lauge Srensen;Saher B Shaker;Marleen de Bruijne.
IEEE Transactions on Medical Imaging (2010)
A Genome-Wide Association Study Identifies Five Loci Influencing Facial Morphology in Europeans
Fan Liu;Fedde van der Lijn;Claudia Schurmann;Gu Zhu.
PLOS Genetics (2012)
Machine learning approaches in medical image analysis: From detection to diagnosis
Marleen de Bruijne.
Medical Image Analysis (2016)
Extraction of Airways From CT (EXACT'09)
Pechin Lo;Bram van Ginneken;Joseph M. Reinhardt;Tarunashree Yavarna.
IEEE Transactions on Medical Imaging (2012)
Transfer Learning Improves Supervised Image Segmentation Across Imaging Protocols
Annegreet van Opbroek;M. Arfan Ikram;Meike W. Vernooij;Marleen de Bruijne.
IEEE Transactions on Medical Imaging (2015)
MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans
Adriënne M. Mendrik;Koen L. Vincken;Hugo J. Kuijf;Marcel Breeuwer.
Computational Intelligence and Neuroscience (2015)
Adapting Active Shape Models for 3D segmentation of tubular structures in medical images.
Marleen de Bruijne;Bram van Ginneken;Max A. Viergever;Wiro J. Niessen.
information processing in medical imaging (2003)
2D–3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models
N. Baka;B.L. Kaptein;M. de Bruijne;M. de Bruijne;T. van Walsum.
Medical Image Analysis (2011)
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