What is he best known for?
The fields of study he is best known for:
- Cancer
- Internal medicine
- Statistics
Hugo J.W.L. Aerts mostly deals with Artificial intelligence, Radiomics, Medical imaging, Image processing and Feature.
Hugo J.W.L. Aerts combines subjects such as Cancer, Lung, Oncology and Pathology with his study of Radiomics.
His work carried out in the field of Medical imaging brings together such families of science as Bioinformatics, Medical physics, Machine learning, Workflow and Sampling.
His study in Feature is interdisciplinary in nature, drawing from both Image segmentation, Data mining, Tomography and Feature extraction, Pattern recognition.
His work in Pattern recognition addresses subjects such as Field, which are connected to disciplines such as High-Throughput Screening Assays and Proteomics methods.
His study explores the link between Deep learning and topics such as Domain that cross with problems in Radiology.
His most cited work include:
- Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach (2051 citations)
- Radiomics: extracting more information from medical images using advanced feature analysis. (1697 citations)
- Radiomics: extracting more information from medical images using advanced feature analysis. (1697 citations)
What are the main themes of his work throughout his whole career to date?
Hugo J.W.L. Aerts focuses on Internal medicine, Radiology, Lung cancer, Oncology and Radiomics.
His work in Internal medicine covers topics such as Cardiology which are related to areas like Framingham Risk Score.
His Oncology course of study focuses on Pathology and Area under the curve and Magnetic resonance imaging.
To a larger extent, he studies Artificial intelligence with the aim of understanding Radiomics.
The concepts of his Artificial intelligence study are interwoven with issues in Field and Machine learning.
His study deals with a combination of Medical imaging and Image processing.
He most often published in these fields:
- Internal medicine (34.51%)
- Radiology (29.93%)
- Lung cancer (27.46%)
What were the highlights of his more recent work (between 2019-2021)?
- Internal medicine (34.51%)
- Artificial intelligence (27.46%)
- Radiology (29.93%)
In recent papers he was focusing on the following fields of study:
Internal medicine, Artificial intelligence, Radiology, Radiomics and Lung cancer are his primary areas of study.
His Internal medicine research is multidisciplinary, relying on both Oncology and Cardiology.
His study in the field of Deep learning and Medical imaging is also linked to topics like Set.
His Medical imaging research includes themes of Imaging phantom, Positron emission tomography, Tomography, Data set and Pattern recognition.
His Radiomics study combines topics from a wide range of disciplines, such as Magnetic resonance imaging, Logistic regression, Data management and Metadata.
Hugo J.W.L. Aerts interconnects Lung and Cohort in the investigation of issues within Lung cancer.
Between 2019 and 2021, his most popular works were:
- The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping (376 citations)
- The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping (376 citations)
- Transparency and reproducibility in artificial intelligence. (23 citations)
In his most recent research, the most cited papers focused on:
- Cancer
- Internal medicine
- Statistics
His primary scientific interests are in Artificial intelligence, Radiology, Transparency, Chemoradiotherapy and Colorectal cancer.
His Medical imaging study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Set and Image processing, bridging the gap between disciplines.
His Radiology research is multidisciplinary, incorporating perspectives in Cancer screening, Framingham Risk Score and Incidence.
Hugo J.W.L. Aerts integrates many fields, such as Transparency and engineering, in his works.
His work carried out in the field of Chemoradiotherapy brings together such families of science as Image segmentation, Magnetic resonance imaging, Diffusion MRI, Intraclass correlation and Radiomics.
His Colorectal cancer study frequently draws connections between related disciplines such as Logistic regression.
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