What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
Marc Niethammer mainly focuses on Artificial intelligence, Computer vision, Image registration, Algorithm and Pattern recognition.
The study incorporates disciplines such as Topological data analysis and Histology in addition to Artificial intelligence.
His Computer vision study combines topics from a wide range of disciplines, such as Laplace transform and Geodesic.
His Image registration study combines topics in areas such as Displacement field, Medical imaging, Image processing, Regularization and Image domain.
His Algorithm research includes themes of Binary number, Cut, Smoothness, Markov random field and Plane.
His research in Pattern recognition intersects with topics in White matter, Diffusion MRI, Fractional anisotropy, Nuclear medicine and Cartilage.
His most cited work include:
- A method for normalizing histology slides for quantitative analysis (436 citations)
- Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging (300 citations)
- Quicksilver: Fast predictive image registration – A deep learning approach (253 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Image registration and Segmentation.
Marc Niethammer has included themes like Magnetic resonance imaging and Atlas in his Artificial intelligence study.
Marc Niethammer interconnects Geodesic and Medical imaging in the investigation of issues within Computer vision.
His Pattern recognition study frequently draws parallels with other fields, such as Representation.
His work deals with themes such as Large deformation diffeomorphic metric mapping, Diffeomorphism, Focus, Algorithm and Similarity measure, which intersect with Image registration.
The Image study combines topics in areas such as Series and Joint.
He most often published in these fields:
- Artificial intelligence (59.18%)
- Computer vision (29.39%)
- Pattern recognition (26.12%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (59.18%)
- Deep learning (11.84%)
- Pattern recognition (26.12%)
In recent papers he was focusing on the following fields of study:
Marc Niethammer mainly investigates Artificial intelligence, Deep learning, Pattern recognition, Image registration and Segmentation.
His Artificial intelligence research includes elements of Machine learning, Computer vision and Atlas.
His work on Iterative reconstruction as part of general Computer vision study is frequently linked to Tomosynthesis, bridging the gap between disciplines.
His Deep learning study incorporates themes from Artificial neural network, Diffeomorphism, Mathematical optimization and Training set.
His Pattern recognition research is multidisciplinary, relying on both Margin, Electrocardiography and Representation.
The concepts of his Image registration study are interwoven with issues in Metric, Large deformation diffeomorphic metric mapping, Magnetic resonance imaging, Transformation and Joint.
Between 2018 and 2021, his most popular works were:
- DeepAtlas: Joint Semi-supervised Learning of Image Registration and Segmentation (24 citations)
- Learning Representations of Persistence Barcodes (19 citations)
- Networks for Joint Affine and Non-Parametric Image Registration (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Marc Niethammer spends much of his time researching Artificial intelligence, Deep learning, Image registration, Pattern recognition and Magnetic resonance imaging.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Differentiable function, Kernel density estimation and Persistent homology.
His Deep learning research integrates issues from Topological data analysis and Atlas.
His work carried out in the field of Image registration brings together such families of science as Diffeomorphism and Metric.
His work on Segmentation and Convolutional neural network as part of general Pattern recognition research is frequently linked to Upstream, thereby connecting diverse disciplines of science.
In Magnetic resonance imaging, Marc Niethammer works on issues like Vector field, which are connected to Algorithm.
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