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Transductive Image Segmentation: Self-training and Effect of Uncertainty Estimation

Kamnitsas K, Winzeck S, Kornaropoulos EN, Whitehouse D, Englman C, Phyu P, Pao N, Menon DK, Rueckert D, Das T, Newcombe VFJ, Glocker B (2021)

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Publication Type: Conference contribution

Publication year: 2021

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12968 LNCS

Pages Range: 79-89

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

Event location: Virtual, Online

ISBN: 9783030877217

DOI: 10.1007/978-3-030-87722-4_8

Abstract

Semi-supervised learning (SSL) uses unlabeled data during training to learn better models. Previous studies on SSL for medical image segmentation focused mostly on improving model generalization to unseen data. In some applications, however, our primary interest is not generalization but to obtain optimal predictions on a specific unlabeled database that is fully available during model development. Examples include population studies for extracting imaging phenotypes. This work investigates an often overlooked aspect of SSL, transduction. It focuses on the quality of predictions made on the unlabeled data of interest when they are included for optimization during training, rather than improving generalization. We focus on the self-training framework and explore its potential for transduction. We analyze it through the lens of Information Gain and reveal that learning benefits from the use of calibrated or under-confident models. Our extensive experiments on a large MRI database for multi-class segmentation of traumatic brain lesions shows promising results when comparing transductive with inductive predictions. We believe this study will inspire further research on transductive learning, a well-suited paradigm for medical image analysis.

Involved external institutions

How to cite

APA:

Kamnitsas, K., Winzeck, S., Kornaropoulos, E.N., Whitehouse, D., Englman, C., Phyu, P.,... Glocker, B. (2021). Transductive Image Segmentation: Self-training and Effect of Uncertainty Estimation. In Shadi Albarqouni, M. Jorge Cardoso, Qi Dou, Konstantinos Kamnitsas, Bishesh Khanal, Islem Rekik, Nicola Rieke, Debdoot Sheet, Sotirios Tsaftaris, Daguang Xu, Ziyue Xu (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 79-89). Virtual, Online: Springer Science and Business Media Deutschland GmbH.

MLA:

Kamnitsas, Konstantinos, et al. "Transductive Image Segmentation: Self-training and Effect of Uncertainty Estimation." Proceedings of the 3rd MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the 1st MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Shadi Albarqouni, M. Jorge Cardoso, Qi Dou, Konstantinos Kamnitsas, Bishesh Khanal, Islem Rekik, Nicola Rieke, Debdoot Sheet, Sotirios Tsaftaris, Daguang Xu, Ziyue Xu, Springer Science and Business Media Deutschland GmbH, 2021. 79-89.

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