What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Statistics
His main research concerns Artificial intelligence, Computer vision, Segmentation, Image segmentation and Pattern recognition.
The concepts of his Artificial intelligence study are interwoven with issues in Magnetic resonance imaging and Diffusion MRI.
Paul A. Yushkevich combines subjects such as Algorithm, Atlas and Affine transformation with his study of Computer vision.
His Segmentation study incorporates themes from Dentate gyrus and Neuroscience.
Paul A. Yushkevich is interested in Scale-space segmentation, which is a branch of Image segmentation.
His study focuses on the intersection of Scale-space segmentation and fields such as Active contour model with connections in the field of Visualization, Level set, Machine learning and Reliability.
His most cited work include:
- User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability (4164 citations)
- N4ITK: Improved N3 Bias Correction (2228 citations)
- Multi-Atlas Segmentation with Joint Label Fusion (601 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Segmentation, Pattern recognition, Computer vision and Neuroscience are his primary areas of study.
His Artificial intelligence study combines topics in areas such as Diffusion MRI and Atlas.
His work on Scale-space segmentation and Multi atlas as part of general Segmentation research is often related to Context, thus linking different fields of science.
His Pattern recognition research is multidisciplinary, incorporating elements of White matter and Similarity.
His work in the fields of Hippocampal formation, such as Dentate gyrus and Subiculum, overlaps with other areas such as Protocol.
His Image segmentation study necessitates a more in-depth grasp of Image.
He most often published in these fields:
- Artificial intelligence (43.37%)
- Segmentation (28.32%)
- Pattern recognition (24.73%)
What were the highlights of his more recent work (between 2018-2021)?
- Temporal lobe (20.07%)
- Artificial intelligence (43.37%)
- Neuroscience (20.07%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Temporal lobe, Artificial intelligence, Neuroscience, Pattern recognition and Hippocampal formation.
His biological study spans a wide range of topics, including Anatomy, Disease, Atrophy, Pathology and Neuroimaging.
His work deals with themes such as Magnetic resonance imaging and Computer vision, which intersect with Artificial intelligence.
In his study, which falls under the umbrella issue of Pattern recognition, Scanner is strongly linked to Volume.
He interconnects Perirhinal cortex and Hippocampus in the investigation of issues within Hippocampal formation.
His Image segmentation study in the realm of Segmentation interacts with subjects such as Protocol.
Between 2018 and 2021, his most popular works were:
- User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP (28 citations)
- Progress update from the hippocampal subfields group (20 citations)
- Automated segmentation of medial temporal lobe subregions on in vivo T1-weighted MRI in early stages of Alzheimer's disease. (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Statistics
Paul A. Yushkevich focuses on Hippocampus, Hippocampal formation, Temporal lobe, Artificial intelligence and Segmentation.
His Hippocampal formation research is multidisciplinary, relying on both Perirhinal cortex and Neuroimaging.
The various areas that he examines in his Temporal lobe study include Cognitive psychology, Magnetic resonance imaging, Neurocognitive, Entorhinal cortex and Atrophy.
His Artificial intelligence study integrates concerns from other disciplines, such as Histology, Computer vision and Pattern recognition.
In general Computer vision study, his work on Image segmentation and Active contour model often relates to the realm of Context, thereby connecting several areas of interest.
His studies in Segmentation integrate themes in fields like Reliability, Data science and Medical imaging.
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.
