Project 2.1 - Weakly Labeled Learning

We focus on deep learning with large amounts of images that are only weakly labeled (e.g. only overall diagnosis or treatment outcome is available). We develop techniques that exploit a small set of images with detailed annotations and a large pool of weakly or completely unlabeled data. We exploit shared representations between learning tasks with different localization levels and use active learning where medical experts are asked for feedback on automatically selected cases.

Project Leader

Eindhoven University of Technology
Prof.dr. Josien Pluim Eindhoven University of Technology j.pluim@tue.nl

Co-Applicants

Erasmus Medical Center
Dr. Marleen de Bruijne Erasmus Medical Center marleen.debruijne@erasmusmc.nl
Eindhoven University of Technology
Dr. Mitko Veta Eindhoven University of Technology M.Veta@tue.nl

Researchers

Eindhoven University of Technology
Suzanne Wetstein Eindhoven University of Technology s.c.wetstein@tue.nl
Eindhoven University of Technology
Friso Heslinga Eindhoven University of Technology f.g.heslinga@tue.nl
Erasmus Medical Center
Gerda Bortsova Erasmus Medical Center g.bortsova@erasmusmc.nl

Publications

2020

Gerda Bortsova, Daniel Bos, Florian Dubost, Meike W. Vernooij, M. Kamran Ikram, Gijs van Tulder, Marleen de Bruijne

Automated Assessment of Intracranial Carotid Artery Calcification in Non-Contrast CT Using Deep Learning Journal Article Forthcoming

Forthcoming.

BibTeX

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

2019

Ruwan Tennakoon, Gerda Bortsova, Silas Ørting, Amirali K Gostar, Mathilde MW Wille, Zaigham Saghir, Reza Hoseinnezhad, Marleen de Bruijne, Alireza Bab-Hadiashar

Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem Journal Article

IEEE Transactions on Medical Imaging, 39 (4), pp. 854-865, 2019.

Abstract | Links | BibTeX

Gerda Bortsova, Florian Dubost, Laurens Hogeweg, Ioannis Katramados, Marleen de Bruijne

Semi-supervised medical image segmentation via learning consistency under transformations Inproceedings

International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 810-818, Springer, Cham, 2019.

Abstract | Links | BibTeX

2018

G. Bortsova, F. Dubost, S. Ørting, I. Katramados, L. Hogeweg, L. Thomsen, M. Wille, M. de Bruijne

Deep learning from label proportions for emphysema quantification Inproceedings

International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 768–776, Springer, Cham, 2018.

Abstract | Links | BibTeX