Eindhoven University of Technology
Suzanne Wetstein Eindhoven University of Technology s.c.wetstein@tue.nl

PhD Candidate
E-mail: s.c.wetstein@tue.nl
Phone: +31 40 24 75581
LinkedIn; Google Scholar

 


Suzanne Wetstein is a PhD-candidate at the Medical Image Analysis Group at Eindhoven University of Technology under supervision of Prof. Josien Pluim and Dr. Mitko Veta. Her research is on deep learning applied to histopathological image analysis.

Suzanne has a BSc in Applied Physics from Delft University of Technology and a BSc in Economics and Business from Erasmus University Rotterdam. She did her MSc at VU University, where she studied Business Analytics. During her MSc she studied at Nanyang Technological University in Singapore for half a year to gain more machine learning knowledge. Suzanne concluded her MSc with an internship at ORTEC Consulting, where she worked on machine learning approaches for natural language processing applied to chatbots.

Her research interests include machine learning (deep learning), pattern recognition and medical image analysis.


2020

Suzanne C. Wetstein, Allison M. Onken, Christina Luffman, Gabrielle M. Baker, Michael E. Pyle, Kevin H. Kensler, Ying Liu, Bart Bakker, Ruud Vlutters, Marinus B. van Leeuwen, Laura C. Collins, Stuart J. Schnitt, Josien P. W. Pluim, Rulla M. Tamimi, Yujing J. Heng, Mitko Veta

Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk Journal Article

PLoS ONE, 15 (4), pp. e0231653, 2020.

Abstract | Links | BibTeX

Kevin H. Kensler; Emily Z.F. Liu; Suzanne C. Wetstein; Allison M. Onken; Christina I. Luffman; Gabrielle M. Baker; Laura C. Collins; Stuart J. Schnitt; Vanessa C. Bret-Mounet; Mitko Veta; Josien P.W. Pluim; Ying Liu; Graham A. Colditz; A. Heather Eliassen; Susan E. Hankinson; Rulla M. Tamimi; Yujing J. Heng

Automated quantitative measures of terminal duct lobular unit involution and breast cancer risk Journal Article Forthcoming

Cancer epidemiology, biomarkers & prevention, Forthcoming.

Abstract | Links | BibTeX

Suzanne C Wetstein, Nikolas Stathonikos, Josien PW Pluim, Yujing J Heng, Natalie D ter Hoeve, Celien PH Vreuls, Paul J van Diest, Mitko Veta

Deep Learning-Based Grading of Ductal Carcinoma In Situ in Breast Histopathology Images Journal Article Forthcoming

arXiv, Forthcoming.

Abstract | Links | BibTeX

2019

Suzanne C. Wetstein, Allison M. Onken, Gabrielle M. Baker, Michael E. Pyle, Josien P. W. Pluim, Rulla M. Tamimi, Yujing J. Heng, Mitko Veta

Detection of acini in histopathology slides: towards automated prediction of breast cancer risk Inproceedings

SPIE Medical Imaging, 2019.

Abstract | Links | BibTeX

2020

Suzanne C. Wetstein, Cristina González-Gonzalo, Gerda Bortsova, Bart Liefers, Florian Dubost, Ioannis Katramados, Laurens Hogeweg, Bram van Ginneken, Josien P.W. Pluim, Marleen de Bruijne, Clara I. Sánchez, Mitko Veta

Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors Conference

2020.

Abstract | Links | BibTeX

C. González-Gonzalo, S. C. Wetstein, G. Bortsova, B. Liefers, B. van Ginneken, C. I. Sánchez

Are adversarial attacks an actual threat for deep learning systems in real-world eye disease screening settings? Conference

European Society of Retina Specialists, 2020.

Abstract | Links | BibTeX

2019

Allison M. Onken, Suzanne Wetstein, Michael Pyle, Josien Pluim, Stuart J. Schnitt, Gabrielle M Baker, Laura C. Collins, Rulla Tamimi, Mitko Veta, Yujing Jan Heng

Deep Learning Networks to Segment and Detect Breast Terminal Duct Lobular Units, Acini, and Adipose Tissue: A Step Toward the Automated Analysis of Lobular Involution as a Marker for Breast Cancer Risk Conference

United States and Canadian Academy of Pathology (USCAP), 2019.

Abstract | BibTeX

Christina I. Luffman, Suzanne C. Wetstein, Allison M. Onken, Michael E. Pyle, Kevin H. Kensler, Ying Liu, Josien P. Pluim, Mitko Veta, Stuart J. Schnitt, Rulla M. Tamimi, Gabrielle M. Baker, Laura C. Collins, Yu Jing Heng

Assessing Breast Terminal Duct Lobular Unit Involution: A Computational Pathology Approach Conference

Abstracts and Case Studies From the College of American Pathologists 2019 Annual Meeting (CAP19), 143 (9), Archives of Pathology & Laboratory Medicine, 2019.

Links | BibTeX