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.
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.
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
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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Blobworld: image segmentation using expectation-maximization and its application to image querying
C. Carson;S. Belongie;H. Greenspan;J. Malik.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique
Hayit Greenspan;Bram van Ginneken;Ronald M. Summers.
IEEE Transactions on Medical Imaging (2016)
GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification
Maayan Frid-Adar;Idit Diamant;Eyal Klang;Michal Amitai.
Color- and texture-based image segmentation using EM and its application to content-based image retrieval
S. Belongie;C. Carson;H. Greenspan;J. Malik.
international conference on computer vision (1998)
Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis
Ophir Gozes;Maayan Frid-Adar;Hayit Greenspan;Patrick D. Browning.
arXiv: Image and Video Processing (2020)
Super-Resolution in Medical Imaging
The Computer Journal (2009)
Region-based image querying
C. Carson;S. Belongie;H. Greenspan;J. Malik.
1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries (1997)
Content-Based Image Retrieval in Radiology: Current Status and Future Directions
Ceyhun Burak Akgül;Daniel L. Rubin;Sandy Napel;Christopher F. Beaulieu.
Journal of Digital Imaging (2011)
Image enhancement by nonlinear extrapolation in frequency space
H. Greenspan;C.H. Anderson;S. Akber.
IEEE Transactions on Image Processing (2000)
Synthetic data augmentation using GAN for improved liver lesion classification
Maayan Frid-Adar;Eyal Klang;Michal Amitai;Jacob Goldberger.
international symposium on biomedical imaging (2018)
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