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Projective Latent Interventions for Understanding and Fine-Tuning Classifiers

Hinterreiter A, Streit M, Kainz B (2020)

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Publication Type: Conference contribution

Publication year: 2020

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12446 LNCS

Pages Range: 13-22

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

Event location: Lima, PER

ISBN: 9783030611651

DOI: 10.1007/978-3-030-61166-8_2

Abstract

High-dimensional latent representations learned by neural network classifiers are notoriously hard to interpret. Especially in medical applications, model developers and domain experts desire a better understanding of how these latent representations relate to the resulting classification performance. We present Projective Latent Interventions (PLIs), a technique for retraining classifiers by back-propagating manual changes made to low-dimensional embeddings of the latent space. The back-propagation is based on parametric approximations of distributed stochastic neighbourhood embeddings. PLIs allow domain experts to control the latent decision space in an intuitive way in order to better match their expectations. For instance, the performance for specific pairs of classes can be enhanced by manually separating the class clusters in the embedding. We evaluate our technique on a real-world scenario in fetal ultrasound imaging.

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How to cite

APA:

Hinterreiter, A., Streit, M., & Kainz, B. (2020). Projective Latent Interventions for Understanding and Fine-Tuning Classifiers. In Jaime Cardoso, Wilson Silva, Ricardo Cruz, Hien Van Nguyen, Badri Roysam, Nicholas Heller, Pedro Henriques Abreu, Jose Pereira Amorim, Ivana Isgum, Vishal Patel, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Samaneh Abbasi, Diana Mateus, Emanuele Trucco (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 13-22). Lima, PER: Springer Science and Business Media Deutschland GmbH.

MLA:

Hinterreiter, Andreas, Marc Streit, and Bernhard Kainz. "Projective Latent Interventions for Understanding and Fine-Tuning Classifiers." Proceedings of the 3rd International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the 2nd International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020, Lima, PER Ed. Jaime Cardoso, Wilson Silva, Ricardo Cruz, Hien Van Nguyen, Badri Roysam, Nicholas Heller, Pedro Henriques Abreu, Jose Pereira Amorim, Ivana Isgum, Vishal Patel, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Samaneh Abbasi, Diana Mateus, Emanuele Trucco, Springer Science and Business Media Deutschland GmbH, 2020. 13-22.

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