Generalizability vs. robustness: Investigating medical imaging networks using adversarial examples

Paschali M, Conjeti S, Navarro F, Navab N (2018)

Publication Type: Conference contribution

Publication year: 2018

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 11070 LNCS

Pages Range: 493-501

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Granada, ESP

ISBN: 9783030009274

DOI: 10.1007/978-3-030-00928-1_56

Abstract

In this paper, for the first time, we propose an evaluation method for deep learning models that assesses the performance of a model not only in an unseen test scenario, but also in extreme cases of noise, outliers and ambiguous input data. To this end, we utilize adversarial examples, images that fool machine learning models, while looking imperceptibly different from original data, as a measure to evaluate the robustness of a variety of medical imaging models. Through extensive experiments on skin lesion classification and whole brain segmentation with state-of-the-art networks such as Inception and UNet, we show that models that achieve comparable performance regarding generalizability may have significant variations in their perception of the underlying data manifold, leading to an extensive performance gap in their robustness.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) DE Germany (DE)

How to cite

APA:

Paschali, M., Conjeti, S., Navarro, F., & Navab, N. (2018). Generalizability vs. robustness: Investigating medical imaging networks using adversarial examples. In Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger, Alejandro F. Frangi (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 493-501). Granada, ESP: Springer Verlag.

MLA:

Paschali, Magdalini, et al. "Generalizability vs. robustness: Investigating medical imaging networks using adversarial examples." Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger, Alejandro F. Frangi, Springer Verlag, 2018. 493-501.

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