Paper Info
Title
Transductive Image Segmentation: Self-training and Effect of Uncertainty Estimation
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.
Year
DOI
Venue
2021
10.1007/978-3-030-87722-4_8
DOMAIN ADAPTATION AND REPRESENTATION TRANSFER, AND AFFORDABLE HEALTHCARE AND AI FOR RESOURCE DIVERSE GLOBAL HEALTH (DART 2021)
DocType
Volume
ISSN
Conference
12968
0302-9743
Citations 
PageRank 
References 
0
0.34
0
Authors
12
Name
Order
Citations
PageRank
Konstantinos Kamnitsas136115.18
Stefan Winzeck200.68
Evgenios N. Kornaropoulos300.68
Daniel Whitehouse400.34
Cameron Englman500.34
Poe Phyu600.34
Norman Pao700.34
David K Menon832116.59
Daniel Rueckert99338637.58
Tilak Das1000.34
Virginia F. J. Newcombe1128110.84
Ben Glocker122157119.81