Abstract
Acquiring properly annotated data is expensive in the medical field as it requires experts, time-consuming protocols, and rigorous validation. Active learning attempts to minimize the need for large annotated samples by actively sampling the most informative examples for annotation. These examples contribute significantly to improving the performance of supervised machine learning models, and thus, active learning can play an essential role in selecting the most appropriate information in deep learning-based diagnosis, clinical assessments, and treatment planning. Although some existing works have proposed methods for sampling the best examples for annotation in medical image analysis, they are not task-agnostic and do not use multimodal auxiliary information in the sampler, which has the potential to increase robustness. Therefore, in this work, we propose a Multimodal Variational Adversarial Active Learning (M-VAAL) method that uses auxiliary information from additional modalities to enhance the active sampling. We applied our method to two datasets: i) brain tumor segmentation and multi-label classification using the BraTS2018 dataset, and ii) chest Xray image classification using the COVID-QU-Ex dataset. Our results show a promising direction toward data-efficient learning under limited annotations.
Original language | English |
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Title of host publication | Medical Image Understanding and Analysis - 27th Annual Conference, MIUA 2023, Proceedings |
Editors | Gordon Waiter, Georgios Leontidis, Teresa Morris, Tryphon Lambrou, Nir Oren, Sharon Gordon |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 48-63 |
Number of pages | 16 |
ISBN (Print) | 9783031485923 |
DOIs | |
Publication status | Published - 2 Dec 2023 |
Event | 27th Annual Conference on Medical Image Understanding and Analysis, MIUA 2023 - Aberdeen, United Kingdom Duration: 19 Jul 2023 → 21 Jul 2023 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 14122 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 27th Annual Conference on Medical Image Understanding and Analysis, MIUA 2023 |
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Country/Territory | United Kingdom |
City | Aberdeen |
Period | 19/07/23 → 21/07/23 |
Bibliographical note
Funding Information:Research reported in this publication was supported by the National Institute of General Medical Sciences Award No. R35GM128877 of the National Institutes of Health, and the Office of Advanced Cyber Infrastructure Award No. 1808530 of the National Science Foundation. BB and DS are supported by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) [203145Z/16/Z]; Engineering and Physical Sciences Research Council (EPSRC) [EP/P027938/1, EP/R004080/1, EP/P012841/1]; The Royal Academy of Engineering Chair in Emerging Technologies scheme; and the EndoMapper project by Horizon 2020 FET (GA 863146).
Keywords
- annotation budget
- brain tumor segmentation and classification
- chest X-ray classification
- multimodal active learning