Research Output per year
Organization profile
Introduction / mission
Clinical relevance is our commitment, achieved through close collaboration with selected clinical/industrial partners and advisors.
Organisational profile
The Biomedical Diagnostics (BM/d) Research Laboratory develops algorithms for the interpretation of biomedical signals and data acquired with a variety of sensors, ranging from ultrasound and magnetic resonance imaging, up to electrophysiological and photoplethysmographic recording. The lab positions itself on the edge between data-driven and physics-driven analysis through a continuous effort towards understanding the application domain and modeling the full measurement chain: (patho)physiological sources, sensing physics, and signal acquisition. To this end, research is carried out in tight collaboration with our clinical and industrial partners. The main application areas are oncology, cardiovascular, gynecology-perinatology, sleep, and neuromuscular research. The lab is chaired by Prof. Mischi, supported by a multidisciplinary team integrating expertise in modeling (Dr. Turco), Bayesian inference (Dr. Vullings), and machine learning (Dr. van Sloun).
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Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Profiles
Tom H.G.F. Bakkes, MSc
- Department of Electrical Engineering, Signal Processing Systems - Doctoral Candidate
- Department of Electrical Engineering, Biomedical Diagnostics Lab
- Department of Electrical Engineering, Center for Care & Cure Technology Eindhoven (C3Te)
Person: Prom. : doctoral candidate (PhD)
Research Output
A mobile app for longterm monitoring of narcolepsy symptoms: design, development, and evaluation
Quaedackers, L., de Wit, J., Pillen, S., van Gilst, M., Batalas, N., Lammers, G. J., Markopoulos, P. & Overeem, S., 7 Jan 2020, In : JMIR Mhealth and Uhealth. 8, 1, 14 p., e14939.Research output: Contribution to journal › Article › Academic › peer-review
Open Access
File
Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods
Wildeboer, R. R., van Sloun, R. J. G., Wijkstra, H. & Mischi, M., Jun 2020, In : Computer Methods and Programs in Biomedicine. 189, 15 p., 105316.Research output: Contribution to journal › Review article › Academic › peer-review
2
Citations
(Scopus)
Automated discomfort detection for premature infants in NICU using time-frequency feature-images and CNNs
Sun, Y., Kommers, D., Tan, T., Wang, W., Long, X., Shan, C., van Pul, C., Aarts, R. M., Andriessen, P. & de With, P. H. N., 2020, SPIE Medical Imaging. SPIE, Vol. 11314. 8 p. 11314Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
Prizes
Best Poster Award 2017 in New Directions - Contrast Ultrasound
Rogier Wildeboer (Recipient), 20 Jan 2017
Prize: Other › Career, activity or publication related prizes (lifetime, best paper, poster etc.) › Scientific
Best Poster Award 2018 in New Directions - Contrast Ultrasound
Rogier Wildeboer (Recipient), 19 Jan 2018
Prize: Other › Career, activity or publication related prizes (lifetime, best paper, poster etc.) › Scientific
Activities
National Day on Biomedical Engineering: Artificial Intelligence in Medicine
Anouk van Diepen (Participant)28 Nov 2019 → 29 Nov 2019
Activity: Participating in or organising an event types › Conference › Scientific
Annual IEEE EMBS Benelux Chapter Symposium
Anouk van Diepen (Participant)28 Nov 2019
Activity: Participating in or organising an event types › Conference › Scientific
2019 IEEE International Ultrasonics Symposium, IUS 2019
Rogier R. Wildeboer (Participant)6 Oct 2019 → 9 Oct 2019
Activity: Participating in or organising an event types › Conference › Scientific
Press / Media
-Eindhoven University of Technology: Intelligent lung ultrasound provides crucial support for COVID-19 testing within minutes
14/05/20
2 items of Media coverage
Press/Media: Expert Comment
Intelligent lung ultrasound provides crucial support for COVID-19 testing within minutes
13/05/20 → 14/05/20
3 items of Media coverage
Press/Media: Expert Comment
AI enhanced lung ultrasound for COVID-19 testing
13/05/20
1 item of Media coverage
Press/Media: Expert Comment
Student theses
Adversarial deep learning in super resolution ultrasound
Author: Jeukendrup, K., 31 Jan 2019Supervisor: van Sloun, R. (Supervisor 1)
Student thesis: Master
Estimation of acoustical coefficient of nonlinearity
Author: Zeng, Y., 24 May 2018Supervisor: Mischi, M. (Supervisor 1) & Wijkstra, H. (Supervisor 2)
Student thesis: Master
Machine learning for classification of uterine activity during IVF cycles
Author: Bakkes, T., 13 Dec 2018Supervisor: Mischi, M. (Supervisor 1), Sammali, F. (Supervisor 2) & Schoot, B. (Supervisor 2)
Student thesis: Master