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Researcher
Frederik Maes
- Disciplines:Artificial intelligence, Multimedia processing, Biological system engineering, Signal processing, Other computer engineering, information technology and mathematical engineering, Medical imaging and therapy
Affiliations
- Processing Speech and Images (PSI) (Division)
Responsible
From1 Aug 2020 → Today - Processing Speech and Images (PSI) (Division)
Member
From1 Aug 2020 → Today - Department of Electrical Engineering (ESAT) (Department)
Member
From1 Oct 2004 → 31 Dec 2007 - Radiology (Division)
Member
From1 Oct 2000 → 30 Sep 2004 - ESAT - PSI, Processing Speech and Images (Division)
Member
From1 Oct 1999 → 31 Jul 2020
Projects
1 - 10 of 53
- Engelstalig: precision medicine en care in patients neuromuscular dystrophyFrom1 Oct 2023 → TodayFunding: Own budget, for example: patrimony, inscription fees, gifts
- Compressed sensing for accelerated microstructure imagingFrom1 Oct 2022 → TodayFunding: Own budget, for example: patrimony, inscription fees, gifts
- Machine Learning tool to increase the efficiency of segmentation correction in transcatheter heart intervention planningFrom1 Sep 2022 → TodayFunding: Baekeland
- Evaluating novel radiological and clinical outcome measures in hereditary neuromuscular diseases.From1 Jun 2022 → TodayFunding: Own budget, for example: patrimony, inscription fees, gifts
- Development of AI tools to assist the treatment of colorectal cancerFrom1 Feb 2022 → TodayFunding: Own budget, for example: patrimony, inscription fees, gifts
- AI in medical imaging: from proof-of-concept to daily radiology servicesFrom11 Jan 2022 → TodayFunding: Own budget, for example: patrimony, inscription fees, gifts
- Development of AI based imaging tools for the analysis of lung disease in preclinical rodent modelsFrom22 Nov 2021 → TodayFunding: Own budget, for example: patrimony, inscription fees, gifts
- Learning-based computational strategies for multimodal image analysis of neurovascular diseasesFrom18 Nov 2021 → TodayFunding: FWO fellowships
- Towards more accurate diagnostics in healthcare: AI applied to super-resolution ultrasound imagingFrom1 Nov 2021 → TodayFunding: Department Coordination
- Machine Learning Methods for Multiple Sclerosis Classification and Prediction using MRI Brain ConnectivityFrom25 Jan 2021 → 6 Oct 2022Funding: Own budget, for example: patrimony, inscription fees, gifts
Publications
11 - 20 of 238
- Benefits of automated gross tumor volume segmentation in head and neck cancer using multi-modality information(2023)
Authors: Marilyn Wegge, Frederik Maes, Sandra Nuyts
- Clinical benefits of multi-modality gross tumor volume auto-delineation in head and neck cancer(2023)
Authors: Siri Willems, Frederik Maes, Sandra Nuyts
Pages: S261 - S262 - Deep learning-based IMRT treatment planning on synthetic-CT for ART in NSCLC-patients(2023)
Authors: Dylan Callens, Liesbeth Vandewinckele, Frederik Maes
Pages: S1333 - S1334 - Automated Data-driven Online Detection and Characterization of Endoscopic Lesions using Deep Learning(2023)
Authors: Tom Eelbode, Frederik Maes, Raf Bisschops
- Learning-based Computational Strategies for Treatment Adaptation in Image-guided Radiotherapy(2023)
Authors: Siri Willems, Frederik Maes, Edmond Sterpin
- Evaluation of thigh muscle fat fraction with quantitative MRI in 24 adult LGMDR12 patients over 2 years of follow-up(2022)
Authors: Bram De Wel, Lotte Huysmans, Patrick Dupont, Frederik Maes, Kristl Claeys
Pages: S118 - S118 - Analysis of the proximo-distal gradients of fat replacement along the length of thigh muscles in LGMDR12 patients(2022)
Authors: Bram De Wel, Lotte Huysmans, Frederik Maes, Patrick Dupont, Kristl Claeys
Pages: S118 - S118 - Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI(2022)
Authors: Sofie Tilborghs, Jan Bogaert, Frederik Maes
- Prospective Natural History Study in 24 Adult Patients With LGMDR12 Over 2 Years of Follow-up Quantitative MRI and Clinical Outcome Measures(2022)
Authors: Bram De Wel, Lotte Huysmans, Ronald Peeters, Patrick Dupont, Frederik Maes, Kristl Claeys
Pages: E638 - E649 - Treatment plan prediction for lung IMRT using deep learning based fluence map generation(2022)
Authors: Liesbeth Vandewinckele, Siri Willems, Maarten Lambrecht, Frederik Maes, Wouter Crijns
Pages: 44 - 54
Patents
1 - 1 of 1