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.
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.
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.
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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Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Hugo J W L Aerts;Emmanuel Rios Velazquez;Ralph T H Leijenaar;Chintan Parmar.
Nature Communications (2014)
Radiomics: extracting more information from medical images using advanced feature analysis.
Philippe Lambin;Emmanuel Rios-Velazquez;Ralph Leijenaar;Sara Carvalho.
European Journal of Cancer (2012)
Computational Radiomics System to Decode the Radiographic Phenotype
Joost J.M. van Griethuysen;Joost J.M. van Griethuysen;Joost J.M. van Griethuysen;Andriy Fedorov;Chintan Parmar;Ahmed Hosny.
Cancer Research (2017)
Radiomics: the process and the challenges
Virendra Kumar;Yuhua Gu;Satrajit Basu;Anders Berglund.
Magnetic Resonance Imaging (2012)
Artificial intelligence in radiology
Ahmed Hosny;Chintan Parmar;John Quackenbush;Lawrence H. Schwartz;Lawrence H. Schwartz.
Nature Reviews Cancer (2018)
Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution
Christopher Abbosh;Nicolai J. Birkbak;Nicolai J. Birkbak;Gareth A. Wilson;Gareth A. Wilson;Mariam Jamal-Hanjani.
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
Alex Zwanenburg;Alex Zwanenburg;Martin Vallières;Mahmoud A. Abdalah;Hugo J. W. L. Aerts;Hugo J. W. L. Aerts.
Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution
Nicholas McGranahan;Rachel Rosenthal;Crispin T. Hiley;Crispin T. Hiley;Andrew J. Rowan.
Machine Learning methods for Quantitative Radiomic Biomarkers
Chintan Parmar;Chintan Parmar;Patrick Grossmann;Johan Bussink;Philippe Lambin.
Scientific Reports (2015)
Imaging biomarker roadmap for cancer studies.
James P.B. O'Connor;Eric O. Aboagye;Judith E. Adams;Hugo J.W.L. Aerts;Hugo J.W.L. Aerts.
Nature Reviews Clinical Oncology (2017)
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