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
11 - 20 of 53
- AI-system for real-time automated polyp detection and characterization during colonoscopy: from initial proof of concept to validation and valorization in routine clinical practiceFrom1 Jan 2021 → 31 Dec 2022Funding: IOF - technology validation in lab
- IMPULS-AI-2021From1 Jan 2021 → 31 Dec 2023Funding: Department General Affairs and Finance
- Development and Clinical Implementation of Artificial Intellgence in routine colonoscopy to improve patient management in colonic diseases.From1 Jan 2021 → TodayFunding: FWO research project (including WEAVE projects)
- Benefits of deep learning algorithms for delineation of target volumes in radiotherapy treatment for head and neck cancer.From1 Aug 2020 → TodayFunding: FWO Strategic Basic Research Grant, Foundations, funds and other with scientific goal
- Interactive learning in medical imaging AI: exploiting domain knowledge provided by on-line user feedbackFrom29 May 2020 → TodayFunding: Own budget, for example: patrimony, inscription fees, gifts
- Robust Deep Learning Methods for MR(S)I Characterisation of Brain LesionsFrom20 May 2020 → 1 Feb 2024Funding: Own budget, for example: patrimony, inscription fees, gifts
- Implementation of Artificial Intelligence in Colonoscopic DiagnosticsFrom1 Nov 2019 → TodayFunding: FWO Strategic Basic Research Grant
- TOWARDS TRIAL READINESS IN HEREDITARY NEUROMUSCULAR DISEASES: Developing accurate, feasible and non-invasive outcome measures.From1 Oct 2019 → 1 Oct 2023Funding: FWO fellowships
- Data-driven microstructure imaging with multi-dimensional diffusion MRI in early brain developmentFrom1 Oct 2019 → 30 Sep 2022Funding: FWO fellowships
- Brain Lesion Segmentation and Detection on Multi-parametric MRI Data: Marrying Deep Learning with Low-rank FactorizationFrom15 Jun 2019 → 15 May 2023Funding: Own budget, for example: patrimony, inscription fees, gifts
Publications
1 - 10 of 238
- Lessons for future clinical trials in adults with Becker muscular dystrophy: Disease progression detected by muscle magnetic resonance imaging, clinical and patient-reported outcome measures(2024)
Authors: Lotte Huysmans, Liesbeth De Waele, Frederik Maes, Patrick Dupont, Kristl Claeys
- Robust Deep Learning Methods for MR(S)I Characterisation of Brain Lesions(2024)
Authors: Mladen Rakic, Frederik Maes
- The implementation of Artificial Intelligence in colonoscopic diagnosis(2023)
Authors: Pieter Sinonquel, Raf Bisschops, Frederik Maes, Séverine Vermeire
- Brain Lesion Segmentation and Detection on Multi-parametric MRI Data: Marrying Deep Learning with Low-rank Factorization(2023)
Authors: Pooya Ashtari, Sabine Van Huffel, Frederik Maes
- Deep learning based MLC aperture and monitor unit prediction as a warm start for breast VMAT optimisation(2023)
Authors: Liesbeth Vandewinckele, Caroline Weltens, Frederik Maes, Wouter Crijns
- Deep learning for prediction of future endoscopic disease activity in ulcerative colitis(2023)
Authors: Matthew Blaschko, Tom Eelbode, Frederik Maes, Raf Bisschops
Pages: 1 - 7 - Towards trial readiness in hereditary neuromuscular diseases(2023)
Authors: Bram De Wel, Kristl Claeys, Frederik Maes, Patrick Dupont, Koen Poesen
- Deep learning pipeline for quality filtering of MRSI spectra(2023)
Authors: Mladen Rakic, Frederik Maes
- Histopathological correlations and fat replacement imaging patterns in recessive limb-girdle muscular dystrophy type 12(2023)
Authors: Bram De Wel, Lotte Huysmans, Christophe Depuydt, Ronald Peeters, Dietmar Thal, Patrick Dupont, Frederik Maes, Kristl Claeys
Pages: 1468 - 1481 - Automated MRI quantification of volumetric per-muscle fat fractions in the proximal leg of patients with muscular dystrophies(2023)
Authors: Lotte Huysmans, Bram De Wel, Kristl Claeys, Frederik Maes
Patents
1 - 1 of 1