What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Magnetic resonance imaging
- Computer vision
Aaron Carass focuses on Artificial intelligence, Segmentation, Computer vision, Magnetic resonance imaging and Image processing.
The concepts of his Artificial intelligence study are interwoven with issues in Hyperintensity and Atlas.
Segmentation is the subject of his research, which falls under Pattern recognition.
His study in the fields of Image under the domain of Computer vision overlaps with other disciplines such as Cerebrum.
His research in Magnetic resonance imaging intersects with topics in Brain segmentation, Cerebrospinal fluid and Cortical surface.
In Image processing, Aaron Carass works on issues like Software, which are connected to Brain mri and Tissue segmentation.
His most cited work include:
- Longitudinal changes in cortical thickness associated with normal aging. (212 citations)
- Retinal layer segmentation of macular OCT images using boundary classification (199 citations)
- Longitudinal multiple sclerosis lesion segmentation: Resource and challenge. (137 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Segmentation, Computer vision, Magnetic resonance imaging and Pattern recognition are his primary areas of study.
His study brings together the fields of Optical coherence tomography and Artificial intelligence.
His studies in Segmentation integrate themes in fields like Image processing, Random forest, Boundary and Image quality.
His work in Computer vision tackles topics such as Medical imaging which are related to areas like Modality and Consistency.
The study incorporates disciplines such as Image synthesis, Cerebrospinal fluid, Pulse sequence, Resolution and Superresolution in addition to Magnetic resonance imaging.
His Pattern recognition study combines topics from a wide range of disciplines, such as Diffusion MRI, Data mining and Image translation.
He most often published in these fields:
- Artificial intelligence (75.41%)
- Segmentation (50.82%)
- Computer vision (44.26%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (75.41%)
- Pattern recognition (30.60%)
- Segmentation (50.82%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Computer vision.
Aaron Carass has researched Artificial intelligence in several fields, including Magnetic resonance imaging and Measure.
Many of his research projects under Pattern recognition are closely connected to Regression with Regression, tying the diverse disciplines of science together.
His Segmentation research is multidisciplinary, incorporating elements of Pixel, Equivariant map and Data-driven.
His Deep learning research focuses on Image segmentation and how it connects with Test data and Domain adaptation.
His biological study spans a wide range of topics, including Autoencoder and Optical coherence tomography.
Between 2019 and 2021, his most popular works were:
- Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis (15 citations)
- Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization (7 citations)
- A Disentangled Latent Space for Cross-Site MRI Harmonization (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Magnetic resonance imaging
- Machine learning
Aaron Carass mainly investigates Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Image translation.
As part of the same scientific family, Aaron Carass usually focuses on Artificial intelligence, concentrating on Measure and intersecting with Image, White matter lesion and Machine learning.
In general Pattern recognition study, his work on Training set and Convolutional neural network often relates to the realm of Constrained optimization, thereby connecting several areas of interest.
Aaron Carass has included themes like Ground truth and Intensity in his Segmentation study.
His Deep learning research includes elements of Representation and Computer vision.
His work deals with themes such as Task and Standard test image, which intersect with Image translation.
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