What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Operating system
Henning Müller mainly investigates Information retrieval, Image retrieval, Task, Artificial intelligence and Automatic image annotation.
His Information retrieval study integrates concerns from other disciplines, such as Clef, Content-based image retrieval, Relevance feedback, Contextual image classification and Modality.
His study in the fields of Visual Word under the domain of Image retrieval overlaps with other disciplines such as Context.
His research in Task intersects with topics in Test, Ground truth and Multimedia information retrieval.
His work carried out in the field of Artificial intelligence brings together such families of science as Rehabilitation robotics, Machine learning, Computer vision and Natural language processing.
His work deals with themes such as Variety, Field and Digital image, which intersect with Automatic image annotation.
His most cited work include:
- A review of content-based image retrieval systems in medical applications—clinical benefits and future directions (1276 citations)
- Performance evaluation in content-based image retrieval: overview and proposals (483 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?
Henning Müller focuses on Information retrieval, Artificial intelligence, Image retrieval, Task and Pattern recognition.
His research investigates the connection between Information retrieval and topics such as Content-based image retrieval that intersect with problems in Data mining.
His research in Artificial intelligence tackles topics such as Computer vision which are related to areas like Medical imaging.
Henning Müller focuses mostly in the field of Image retrieval, narrowing it down to topics relating to Multimedia and, in certain cases, World Wide Web.
His work on Task is being expanded to include thematically relevant topics such as Modality.
His study connects Feature and Pattern recognition.
He most often published in these fields:
- Information retrieval (38.95%)
- Artificial intelligence (40.47%)
- Image retrieval (29.34%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (40.47%)
- Pattern recognition (17.03%)
- Deep learning (9.78%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Deep learning, Data science and Convolutional neural network.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Regression.
His research integrates issues of Ground truth, Image, Texture and Tuberculosis in his study of Pattern recognition.
His work in Deep learning tackles topics such as Feature extraction which are related to areas like Contextual image classification.
Henning Müller interconnects Variety and Field in the investigation of issues within Data science.
His Clef study in the realm of Task connects with subjects such as Context.
Between 2017 and 2021, his most popular works were:
- The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping (376 citations)
- Large-scale retrieval for medical image analytics: A comprehensive review. (88 citations)
- Why rankings of biomedical image analysis competitions should be interpreted with care (87 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Operating system
Henning Müller mainly focuses on Artificial intelligence, Deep learning, Pattern recognition, Convolutional neural network and Data science.
His Artificial intelligence research integrates issues from Machine learning and Natural language processing.
In his research, Image retrieval is intimately related to Image fusion, which falls under the overarching field of Natural language processing.
His research in Image retrieval is mostly concerned with Automatic image annotation.
His study focuses on the intersection of Deep learning and fields such as Feature extraction with connections in the field of Contextual image classification.
The study incorporates disciplines such as Grading, Tomography, Digital image and Gleason grading in addition to Pattern recognition.
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.
