What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Radiology
- Internal medicine
His primary scientific interests are in Radiology, Angiography, Tomography, Image processing and Artificial intelligence.
Many of his studies on Radiology involve topics that are commonly interrelated, such as Spiral.
His Angiography research includes themes of Renal artery, Spiral ct and Aorta.
He interconnects Imaging phantom, Nuclear medicine, Volume, Colonoscopy and Ct technique in the investigation of issues within Tomography.
His work deals with themes such as Poisson distribution, Magnitude, Mathematical optimization and Noise, which intersect with Image processing.
His biological study spans a wide range of topics, including Tree, Computer vision and Pattern recognition.
His most cited work include:
- Comparison and Evaluation of Retrospective Intermodality Brain Image Registration Techniques (827 citations)
- Perspective volume rendering of CT and MR images: applications for endoscopic imaging. (378 citations)
- The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping (376 citations)
What are the main themes of his work throughout his whole career to date?
Sandy Napel spends much of his time researching Artificial intelligence, Radiology, Computer vision, Nuclear medicine and Medical imaging.
In his study, which falls under the umbrella issue of Artificial intelligence, Feature is strongly linked to Pattern recognition.
His research in Radiology is mostly focused on Angiography.
His Projection and Thresholding study in the realm of Computer vision connects with subjects such as Set.
His Nuclear medicine research integrates issues from Tomography and Computed tomographic, Computed tomography.
Sandy Napel has researched Medical imaging in several fields, including Image registration and Computed radiography.
He most often published in these fields:
- Artificial intelligence (34.38%)
- Radiology (28.12%)
- Computer vision (23.83%)
What were the highlights of his more recent work (between 2016-2021)?
- Radiomics (5.47%)
- Radiology (28.12%)
- Artificial intelligence (34.38%)
In recent papers he was focusing on the following fields of study:
His main research concerns Radiomics, Radiology, Artificial intelligence, Lung cancer and Feature.
His research in Radiology is mostly concerned with Positron emission tomography.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Imaging interpretation and Pattern recognition.
His Pattern recognition research is multidisciplinary, incorporating elements of Visualization, Cine mri and Medical imaging.
His study in Lung cancer is interdisciplinary in nature, drawing from both Cancer and Standardized uptake value.
His Feature research includes elements of Image processing, Brain tumor, Tomography, Feature extraction and Data set.
Between 2016 and 2021, his most popular works were:
- The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping (376 citations)
- Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? (323 citations)
- A large annotated medical image dataset for the development and evaluation of segmentation algorithms (203 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Radiology
- Internal medicine
The scientist’s investigation covers issues in Artificial intelligence, Lung cancer, Segmentation, Deep learning and Pattern recognition.
His Artificial intelligence study incorporates themes from Brain tumor and Imaging diagnosis.
In the field of Segmentation, his study on Image segmentation overlaps with subjects such as Initialization and Wilcoxon signed-rank test.
The Deep learning study combines topics in areas such as Image noise, Magnetic resonance imaging, Convolutional neural network and Computer vision.
The study incorporates disciplines such as Similarity, Energy functional, Medical imaging, Positron emission tomography and Tomography in addition to Pattern recognition.
His Feature study combines topics in areas such as Image processing, Imaging phantom, Data set and Imaging interpretation.
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