What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Image segmentation and Image retrieval.
His Artificial intelligence research focuses on Deep learning, Image, Convolutional neural network, Feature extraction and Cognitive neuroscience of visual object recognition.
His work carried out in the field of Convolutional neural network brings together such families of science as Artificial neural network and Visualization.
His research integrates issues of Liver lesion and Computed tomography in his study of Pattern recognition.
His Automatic image annotation and Content-based image retrieval study in the realm of Image retrieval interacts with subjects such as Unscented transform and Divergence.
His study looks at the intersection of Segmentation and topics like Magnetic resonance imaging with Feature.
His most cited work include:
- Blobworld: image segmentation using expectation-maximization and its application to image querying (1385 citations)
- Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique (929 citations)
- Color- and texture-based image segmentation using EM and its application to content-based image retrieval (455 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Deep learning.
All of his Artificial intelligence and Image segmentation, Medical imaging, Image, Feature extraction and Convolutional neural network investigations are sub-components of the entire Artificial intelligence study.
Hayit Greenspan works mostly in the field of Pattern recognition, limiting it down to topics relating to Image retrieval and, in certain cases, Information retrieval, as a part of the same area of interest.
His biological study spans a wide range of topics, including Supervised learning and Support vector machine.
His Segmentation research is multidisciplinary, incorporating perspectives in Lesion, Radiography, Magnetic resonance imaging, Mixture model and Voxel.
He interconnects Liver lesion and Synthetic data in the investigation of issues within Deep learning.
He most often published in these fields:
- Artificial intelligence (79.68%)
- Pattern recognition (50.60%)
- Computer vision (39.04%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (79.68%)
- Pattern recognition (50.60%)
- Deep learning (17.93%)
In recent papers he was focusing on the following fields of study:
Hayit Greenspan mostly deals with Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Medical imaging.
His Artificial intelligence research incorporates themes from Machine learning and Liver lesion.
His Pattern recognition study integrates concerns from other disciplines, such as Pixel and Image.
The various areas that Hayit Greenspan examines in his Deep learning study include Interpretability, Representation, Radiology and Computer vision.
The Computer vision study combines topics in areas such as Endotracheal tube and Intubation.
His Segmentation study also includes
- Radiography that intertwine with fields like Image processing and Remote patient monitoring,
- Adversarial system and related Supervised learning, Image synthesis and Translation.
Between 2017 and 2021, his most popular works were:
- GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification (452 citations)
- Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis. (291 citations)
- Synthetic data augmentation using GAN for improved liver lesion classification (213 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Artificial intelligence, Pattern recognition, Deep learning, Convolutional neural network and Segmentation are his primary areas of study.
His studies deal with areas such as Multimedia and Liver lesion as well as Artificial intelligence.
His Pattern recognition research incorporates elements of Pixel, Image, Computed tomography and Identification.
His research in Deep learning focuses on subjects like Radiology, which are connected to Analytics.
Hayit Greenspan usually deals with Convolutional neural network and limits it to topics linked to Voxel and Similarity, Ground truth, Algorithm and Normalization.
In his research, Image processing, Pneumonia, Jaccard index and Field is intimately related to Radiography, which falls under the overarching field of Segmentation.
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