What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Lena Maier-Hein spends much of her time researching Artificial intelligence, Computer vision, Medical imaging, Data science and Augmented reality.
The concepts of her Artificial intelligence study are interwoven with issues in Machine learning and Engineering drawing.
Lena Maier-Hein combines subjects such as Computer-Assisted Intervention, Surface reconstruction, Visualization, Robustness and Product with her study of Computer vision.
Her Medical imaging research is multidisciplinary, incorporating elements of Image registration, Percutaneous, Image-Guided Therapy and Scanner.
Her Data science research incorporates elements of Ontology, Radiomics and Knowledge management.
Her work in Augmented reality addresses issues such as Image-guided surgery, which are connected to fields such as Health informatics, Percutaneous nephrolithotomy, Surgery, 3D reconstruction and Laparoscopic surgery.
Her most cited work include:
- The Medical Imaging Interaction Toolkit: challenges and advances : 10 years of open-source development. (259 citations)
- Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery. (205 citations)
- Electromagnetic tracking in medicine--a review of technology, validation, and applications. (204 citations)
What are the main themes of her work throughout her whole career to date?
Lena Maier-Hein spends much of her time researching Artificial intelligence, Computer vision, Multispectral image, Medical imaging and Segmentation.
Her study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Pattern recognition.
As a member of one scientific family, Lena Maier-Hein mostly works in the field of Computer vision, focusing on Visualization and, on occasion, Augmented reality.
Her research in Multispectral image intersects with topics in Estimation theory, Functional imaging and Biomedical engineering.
Her work in the fields of Image segmentation overlaps with other areas such as Set.
Her Deep learning research is multidisciplinary, relying on both Convolutional neural network and Photoacoustic imaging in biomedicine.
She most often published in these fields:
- Artificial intelligence (51.68%)
- Computer vision (33.61%)
- Multispectral image (16.81%)
What were the highlights of her more recent work (between 2019-2021)?
- Artificial intelligence (51.68%)
- Photoacoustic imaging in biomedicine (9.66%)
- Deep learning (10.50%)
In recent papers she was focusing on the following fields of study:
Lena Maier-Hein focuses on Artificial intelligence, Photoacoustic imaging in biomedicine, Deep learning, Computer vision and Multispectral image.
Her Artificial intelligence research includes themes of Machine learning and Pattern recognition.
Lena Maier-Hein interconnects Cadaver and Convolutional neural network in the investigation of issues within Computer vision.
The Multispectral image study combines topics in areas such as Lambda, Wavelength, Absorption and Measure.
Her research investigates the connection between Segmentation and topics such as Robustness that intersect with issues in Test data.
Her biological study spans a wide range of topics, including Iterative reconstruction and Medical imaging.
Between 2019 and 2021, her most popular works were:
- BIAS: Transparent reporting of biomedical image analysis challenges. (14 citations)
- 2018 Robotic Scene Segmentation Challenge (13 citations)
- Surgical spectral imaging (12 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Her scientific interests lie mostly in Artificial intelligence, Field, Photoacoustic imaging in biomedicine, Machine learning and Deep learning.
Lena Maier-Hein focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Computer vision and, in certain cases, Convolutional neural network.
Her Photoacoustic imaging in biomedicine study combines topics in areas such as Lambda, Measure, Computational physics and Absorption.
Her research integrates issues of Imaging phantom, Spectral imaging, Hyperspectral imaging and White light in her study of Machine learning.
The study incorporates disciplines such as Contrast, Degrees of freedom and Fluoroscopy in addition to Deep learning.
Lena Maier-Hein has included themes like Test data and Robustness in her Segmentation study.
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