What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Cancer
His main research concerns Artificial intelligence, Radiology, Ontology, Magnetic resonance imaging and Information retrieval.
His biological study spans a wide range of topics, including Machine learning, Computer vision and Pattern recognition.
The Radiology study combines topics in areas such as Breast imaging, Mammography, Predictive value of tests and Readability.
He has included themes like Controlled vocabulary, World Wide Web and Natural language processing in his Ontology study.
His Magnetic resonance imaging research includes elements of Cirrhosis, Nuclear magnetic resonance, Polymer and Phases of clinical research.
His studies deal with areas such as Metadata, Decision support system, Image retrieval, Automatic image annotation and Workflow as well as Information retrieval.
His most cited work include:
- Comprehensivemolecular characterization of clear cell renal cell carcinoma (1909 citations)
- Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease (863 citations)
- BioPortal: ontologies and integrated data resources at the click of a mouse (675 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Radiology, Pattern recognition, Information retrieval and Ontology.
His work in Artificial intelligence addresses subjects such as Machine learning, which are connected to disciplines such as Mammography.
His studies in Pattern recognition integrate themes in fields like Feature and Medical imaging.
His Information retrieval study integrates concerns from other disciplines, such as Annotation and Automatic image annotation, Image retrieval.
His work deals with themes such as Controlled vocabulary, World Wide Web and Semantic Web, which intersect with Ontology.
Daniel L. Rubin has researched Magnetic resonance imaging in several fields, including Internal medicine, Nuclear medicine and Oncology.
He most often published in these fields:
- Artificial intelligence (36.74%)
- Radiology (15.87%)
- Pattern recognition (13.36%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (36.74%)
- Deep learning (9.81%)
- Pattern recognition (13.36%)
In recent papers he was focusing on the following fields of study:
Daniel L. Rubin mainly focuses on Artificial intelligence, Deep learning, Pattern recognition, Segmentation and Convolutional neural network.
His research in Artificial intelligence intersects with topics in Machine learning, Mammography and Natural language processing.
His research integrates issues of Transfer of learning, Clinical trial, Radiology and Data science in his study of Deep learning.
His Pattern recognition research incorporates elements of Annotation, Salient and Robustness.
His work carried out in the field of Segmentation brings together such families of science as Geographic atrophy, Optical coherence tomography, Projection, Fluid-attenuated inversion recovery and Ground truth.
His Convolutional neural network research integrates issues from Active contour model, Electroencephalography, Initialization, Boundary and Receiver operating characteristic.
Between 2018 and 2021, his most popular works were:
- Preparing Medical Imaging Data for Machine Learning. (72 citations)
- Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs. (69 citations)
- Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. (47 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Cancer
The scientist’s investigation covers issues in Artificial intelligence, Deep learning, Pattern recognition, Convolutional neural network and Machine learning.
The study incorporates disciplines such as Mammography, Radiology and Natural language processing in addition to Artificial intelligence.
His Radiology research is multidisciplinary, incorporating elements of False positive paradox, Lung cancer and Carcinoma.
Daniel L. Rubin combines subjects such as Sampling, Image segmentation and Sample size determination with his study of Deep learning.
His Pattern recognition study combines topics from a wide range of disciplines, such as Salient, Optical coherence tomography, Visualization, Focus and Robustness.
The concepts of his Convolutional neural network study are interwoven with issues in Artificial neural network, Software and Receiver operating characteristic.
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