What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Constantinos S. Pattichis mostly deals with Artificial intelligence, Ultrasound, Computer vision, Pattern recognition and Image texture.
His Artificial intelligence study frequently intersects with other fields, such as Machine learning.
Constantinos S. Pattichis interconnects Stroke, Segmentation, Asymptomatic and Normalization in the investigation of issues within Ultrasound.
His work deals with themes such as Ultrasound imaging and Medical imaging, which intersect with Computer vision.
His Pattern recognition research is multidisciplinary, incorporating elements of Speech recognition, Signal, Signal processing and Receiver operating characteristic.
He combines subjects such as Classifier, Feature and Unsupervised learning with his study of Artificial neural network.
His most cited work include:
- Wireless telemedicine systems: an overview (351 citations)
- Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery (263 citations)
- Texture-based classification of atherosclerotic carotid plaques (259 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Ultrasound and Artificial neural network are his primary areas of study.
Many of his studies on Artificial intelligence apply to Machine learning as well.
His biological study spans a wide range of topics, including Ultrasound imaging and Medical imaging.
The various areas that Constantinos S. Pattichis examines in his Pattern recognition study include Speech recognition and Feature.
His Ultrasound study combines topics from a wide range of disciplines, such as Stroke, Asymptomatic and Carotid arteries, Common carotid artery.
His work carried out in the field of Artificial neural network brings together such families of science as Unsupervised learning and Feature.
He most often published in these fields:
- Artificial intelligence (40.91%)
- Computer vision (21.31%)
- Pattern recognition (14.77%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (40.91%)
- Magnetic resonance imaging (3.69%)
- Multimedia (9.66%)
In recent papers he was focusing on the following fields of study:
Constantinos S. Pattichis mainly focuses on Artificial intelligence, Magnetic resonance imaging, Multimedia, Ultrasound and Data science.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Descriptive knowledge and Computer vision.
In the field of Computer vision, his study on 3D reconstruction overlaps with subjects such as Interval.
His Multimedia research is multidisciplinary, incorporating elements of Systems medicine, Real-time computing, Co-creation, Video quality and Virtual reality.
Constantinos S. Pattichis has included themes like Internal carotid artery, Internal medicine and Common carotid artery in his Ultrasound study.
Constantinos S. Pattichis has researched Data science in several fields, including Precision medicine and World Wide Web.
Between 2015 and 2021, his most popular works were:
- What do we need to build explainable AI systems for the medical domain (171 citations)
- Medical telerobotic systems: current status and future trends. (45 citations)
- Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer's Disease: A Methodological Review (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Data science, Video quality, Precision medicine, Artificial intelligence and Mediterranean climate.
His studies in Data science integrate themes in fields like Domain and General Data Protection Regulation.
His study in Video quality is interdisciplinary in nature, drawing from both Data compression, Real-time computing and Multimedia.
Constantinos S. Pattichis usually deals with Multimedia and limits it to topics linked to Coding and Ultrasound and Telemedicine.
His study looks at the relationship between Precision medicine and topics such as Big data, which overlap with Open data and Knowledge extraction.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Machine learning.
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.
