Adaptive Adversarial Samples Based Active Learning for Medical Image Classification

Siteng Ma, Yu An, Jing Wang, Aonghus Lawlor, Ruihai Dong

2023

Abstract

Active learning (AL) is a subset of machine learning, which attempts to minimize the number of required training labels while maximizing the performance of the model. Most current research directions regarding AL focus on the improvement of query strategies. However, efficiently utilizing data may lead to more performance improvements than are thought to be achievable by changing the selection strategy. Thus, we present an adaptive adversarial sample-based approach to query unlabeled samples close to the decision boundary through the adversarial attack. Notably, based on that, we investigate the importance of using existing data effectively in AL by integrating generated adversarial samples according to consistency regularization and leveraging large numbers of unlabeled images via pseudo-labeling with the oracle-annotated instances during training. In addition, we explore an adaptive way to request labels dynamically as the model changes state. The experimental results verify our framework’s effectiveness with a significant improvement over various state-of-the-art methods for multiple medical applications. Our method achieves 3% above the supervised learning accuracy on the Messidor Dataset (the task of Diabetic Retinopathy detection) using only 34% of the whole dataset. We also conducted an extensive study on a histological Breast Cancer Diagnosis Dataset. Our code is available at https://github.com/HelenMa9998/adversarial active learning.

Paper Citation

in Harvard Style

Ma S., An Y., Wang J., Lawlor A. and Dong R. (2023). Adaptive Adversarial Samples Based Active Learning for Medical Image Classification. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 751-758. DOI: 10.5220/0011622100003411

in Bibtex Style

@conference{icpram23,
author={Siteng Ma and Yu An and Jing Wang and Aonghus Lawlor and Ruihai Dong},
title={Adaptive Adversarial Samples Based Active Learning for Medical Image Classification},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={751-758},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011622100003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Adaptive Adversarial Samples Based Active Learning for Medical Image Classification
SN - 978-989-758-626-2
AU - Ma S.
AU - An Y.
AU - Wang J.
AU - Lawlor A.
AU - Dong R.
PY - 2023
SP - 751
EP - 758
DO - 10.5220/0011622100003411