What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Internal medicine
- Statistics
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 most cited work include:
- Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns (252 citations)
- A Genome-Wide Association Study Identifies Five Loci Influencing Facial Morphology in Europeans (231 citations)
- Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis (193 citations)
What are the main themes of her work throughout her whole career to date?
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.
She most often published in these fields:
- Artificial intelligence (82.76%)
- Pattern recognition (63.22%)
- Segmentation (45.98%)
What were the highlights of her more recent work (between 2017-2021)?
- Artificial intelligence (82.76%)
- Pattern recognition (63.22%)
- Segmentation (45.98%)
In recent papers she was focusing on the following fields of study:
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
- Object detection and related MNIST database and Interpolation,
- Random forest that connect with fields like Atelectasis, Cystic fibrosis and Bronchiectasis.
Kernel and Imaging phantom is closely connected to Image quality in her research, which is encompassed under the umbrella topic of Deep learning.
Between 2017 and 2021, her most popular works were:
- Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis (193 citations)
- Gray Matter Age Prediction as a Biomarker for Risk of Dementia (34 citations)
- Gray Matter Age Prediction as a Biomarker for Risk of Dementia (34 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Statistics
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.
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