Research Output per year
Personal profile
Quote
In the fight against cancer, we need to make sure the medical specialists have the right 'weapons'.
Research profile
Fons van der Sommen is an assistant professor at the research group Video Coding & Architectures of the department of Electrical Engineering. He is a passionate scientist and educator, holding a MSc. in Electrical Engineering (2012, cum laude) and a PhD in Computer Vision (2017, cum laude). Driven by a personal tragedy, a passion for science and a desire to contribute to this world, he applies his understanding of machine learning and computer vision in healthcare, where he aims to develop novel assistive technologies for medical doctors, helping them with early diagnosis and effective treatment of disease.
In particular, in the field of Medical Image Analysis, Van der Sommen's research focuses on Computer-Aided Detection and Diagnosis (CADe/CADx) for oncology, striving to increase the detection rates and early diagnosis of developing cancer -- thereby vastly increasing the chances of survival for patients. Towards this goal, he works closely with medical specialists in an interdisciplinary team, aiming to translate medical domain knowledge into technical algorithms and vice versa.
Academic background
Fons van der Sommen studied Electrical Engineering at Eindhoven University of Technology (TU/e, the Netherlands) where he obtained his MSc in 2012 with the distinction cum laude. The year before his father had died of cancer, with motivated him to investigate the feasibility of an algorithm for the computer-aided detection of esophageal cancer. This resulted in three publications on international conferences. Following his Master's studies Van der Sommen continued to work on computer vision methods for esophageal cancer detection in collaboration with the Catharina Hospital (Eindhoven) while pursuing a PhD degree. In 2017 he obtained his doctorate with his thesis 'Computer-aided detection of early Barrett's cancer', again with the distinction cum laude. In 2018, after having been a postdoctoral researcher at TU/e for a year, he was appointed assistant professor at the research group Video Coding & Architectures of the TU/e department of Electrical Engineering. That same year he was awarded the TU/e PhD thesis prize.
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Research Output
Deep learning algorithm detection of Barrett's neoplasia with high accuracy during live endoscopic procedures: a pilot study (with video)
de Groof, A. J., Struyvenberg, M. R., Fockens, K. N., van der Putten, J., van der Sommen, F., Boers, T. G., Zinger, S., Bisschops, R., de With, P. H., Pouw, R. E., Curvers, W. L., Schoon, E. J. & Bergman, J. J. G. H. M., Jun 2020, In : Gastrointestinal Endoscopy. 91, 6, p. 1242-1250 9 p.Research output: Contribution to journal › Article › Academic › peer-review
2
Citations
(Scopus)
Deep-learning system detects neoplasia in patients with Barrett’s Esophagus with higher accuracy than endoscopists in a multi-step training and validation study with benchmarking
de Groof, A. J., Struyvenberg, M. R., van der Putten, J., van der Sommen, F., Fockens, K. N., Curvers, W. L., Zinger, S., Pouw, R. E., Coron, E., Baldaque-Silva, F., Pech, O., Weusten, B., Meining, A., Neuhaus, H., Bisschops, R. R., Dent, J., Schoon, E. J., de With, P. H. N. & Bergman, J. J., Mar 2020, In : Gastroenterology. 158, 4, p. 915-929.e4Research output: Contribution to journal › Article › Academic › peer-review
8
Citations
(Scopus)
Deep principal dimension encoding for the classification of early neoplasia in Barrett's Esophagus with volumetric laser endomicroscopy
van der Putten, J., Struyvenberg, M., de Groof, J., Scheeve, T., Curvers, W., Schoon, E., Bergman, J. J. G. H. M., de With, P. H. N. & van der Sommen, F., Mar 2020, In : Computerized Medical Imaging and Graphics. 80, 10 p., 101701.Research output: Contribution to journal › Article › Academic › peer-review
Open Access
File
Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery
Manni, F., van der Sommen, F., Zinger, S., Shan, C., Holthuizen, R., Lai, M., Burstrom, G., Hoveling, R. J. M., Edstrom, E., Elmi-Terander, A. & de With, P. H. N., 12 Jun 2020, In : Applied Sciences. 10, 12, 19 p., 4078.Research output: Contribution to journal › Article › Academic › peer-review
Open Access
Machine learning in GI endoscopy: practical guidance in how to interpret a novel field
van der Sommen, F., de Groof, J., Struyvenberg, M., van der Putten, J., Boers, T., Fockens, K., Schoon, E. J., Curvers, W., de With, P., Mori, Y., Byrne, M. & Bergman, J. J. G. H. M., 11 May 2020, In : Gut.Research output: Contribution to journal › Review article › Academic › peer-review
Open Access
Prizes
Best Poster Presentation
Fons van der Sommen (Recipient), 10 May 2017
Prize: Other › Career, activity or publication related prizes (lifetime, best paper, poster etc.) › Scientific
Best Poster Presentation
Fons van der Sommen (Recipient), 18 Jun 2013
Prize: Other › Career, activity or publication related prizes (lifetime, best paper, poster etc.) › Scientific
TU/e Best PhD Thesis Award
Fons van der Sommen (Recipient), 21 Sep 2018
Prize: TU/e › Other › Scientific
Activities
- 4 Contributed talk
A novel clinical gland feature for detection of early Barrett’s neoplasia using volumetric laser endomicroscopy
Thom Scheeve (Speaker), Maarten R. Struyvenberg (Member), Wouter L. Curvers (Member), Jeroen de Groof (Member), Erik J. Schoon (Member), Jacques J.G.H.M. Bergman (Member), F. van der Sommen (Member), P.H.N. de With (Member)20 Feb 2019
Activity: Talk or presentation types › Contributed talk › Scientific
Computer-aided classification of colorectal polyps using blue-light and linked-color imaging
Thom Scheeve (Speaker), Ramon-Michel Schreuder (Member), Fons van der Sommen (Member), Joep E.G. IJspeert (Member), Evelien Dekker (Member), Erik J. Schoon (Member), P.H.N. de With (Member)18 Feb 2019
Activity: Talk or presentation types › Contributed talk › Scientific
Computer-aided classification of colorectal polyps
Thom Scheeve (Speaker), Ramon-Michel Schreuder (Member), F. van der Sommen (Member), Erik J. Schoon (Member), P.H.N. de With (Member)5 Apr 2018
Activity: Talk or presentation types › Contributed talk › Scientific
Voorjaarsvergadering Nederlandse Vereniging voor Gastroenterologie, Koningshof, Veldhoven, the Netherlands
Fons van der Sommen (Speaker)22 Mar 2013
Activity: Talk or presentation types › Contributed talk › Scientific
Courses
Press / Media
-Eindhoven University of Technology: Smart software detects early-stage esophageal cancer; The algorithm can prevent invasive surgery and reduces mortality
Peter H.N. de With, Fons van der Sommen & Joost van der Putten
20/02/20
2 items of Media coverage
Press/Media: Expert Comment
Smart software detects early-stage esophageal cancer
Fons van der Sommen & Joost van der Putten
19/02/20
1 item of Media coverage
Press/Media: Expert Comment
New Detection Method Paves the Way for 100% Detection of Esophageal Cancer
Peter H.N. de With & Fons van der Sommen
7/07/16
1 item of Media coverage
Press/Media: Expert Comment
Automatic Computer System Paves Way for 100% Detection of Esophageal Cancer
Peter H.N. de With & Fons van der Sommen
7/07/16
1 item of Media coverage
Press/Media: Expert Comment
New detection method paves the way for 100 percent detection of esophageal cancer
Peter H.N. de With & Fons van der Sommen
5/07/16
1 item of Media coverage
Press/Media: Expert Comment
Student theses
Automatic detection of early cancer in the esophagus using high-definition endoscopy
Author: van der Sommen, F., 31 Aug 2012Supervisor: Zinger, S. (Supervisor 1), de With, P. (Supervisor 2) & Schoon, E. J. (External person) (External coach)
Student thesis: Master
Computer-aided classification of colorectal polyps
Author: Scheeve, T., 29 Mar 2018Supervisor: van der Sommen, F. (Supervisor 1) & Schoon, E. J. (External person) (External coach)
Student thesis: Master
Image features for automated colorectal polyp classification based on clinical prediction models
Author: van Grinsven, M., 13 Dec 2018Supervisor: de With, P. (Supervisor 1) & van der Sommen, F. (Supervisor 2)
Student thesis: Master