TY - GEN

T1 - Semi-supervised Medical Image Classification with Temporal Knowledge-Aware Regularization

AU - Yang, Qiushi

AU - Liu, Xinyu

AU - Chen, Zhen

AU - Ibragimov, Bulat

AU - Yuan, Yixuan

N1 - Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - Semi-supervised learning (SSL) for medical image classification has achieved exceptional success on efficiently exploiting knowledge from unlabeled data with limited labeled data. Nevertheless, recent SSL methods suffer from misleading hard-form pseudo labeling, exacerbating the confirmation bias issue due to rough training process. Moreover, the training schemes excessively depend on the quality of generated pseudo labels, which is vulnerable against the inferior ones. In this paper, we propose TEmporal knowledge-Aware Regularization (TEAR) for semi-supervised medical image classification. Instead of using hard pseudo labels to train models roughly, we design Adaptive Pseudo Labeling (AdaPL), a mild learning strategy that relaxes hard pseudo labels to soft-form ones and provides a cautious training. AdaPL is built on a novel theoretically derived loss estimator, which approximates the loss of unlabeled samples according to the temporal information across training iterations, to adaptively relax pseudo labels. To release the excessive dependency of biased pseudo labels, we take advantage of the temporal knowledge and propose Iterative Prototype Harmonizing (IPH) to encourage the model to learn discriminative representations in an unsupervised manner. The core principle of IPH is to maintain the harmonization of clustered prototypes across different iterations. Both AdaPL and IPH can be easily incorporated into prior pseudo labeling-based models to extract features from unlabeled medical data for accurate classification. Extensive experiments on three semi-supervised medical image datasets demonstrate that our method outperforms state-of-the-art approaches. The code is available at https://github.com/CityU-AIM-Group/TEAR.

AB - Semi-supervised learning (SSL) for medical image classification has achieved exceptional success on efficiently exploiting knowledge from unlabeled data with limited labeled data. Nevertheless, recent SSL methods suffer from misleading hard-form pseudo labeling, exacerbating the confirmation bias issue due to rough training process. Moreover, the training schemes excessively depend on the quality of generated pseudo labels, which is vulnerable against the inferior ones. In this paper, we propose TEmporal knowledge-Aware Regularization (TEAR) for semi-supervised medical image classification. Instead of using hard pseudo labels to train models roughly, we design Adaptive Pseudo Labeling (AdaPL), a mild learning strategy that relaxes hard pseudo labels to soft-form ones and provides a cautious training. AdaPL is built on a novel theoretically derived loss estimator, which approximates the loss of unlabeled samples according to the temporal information across training iterations, to adaptively relax pseudo labels. To release the excessive dependency of biased pseudo labels, we take advantage of the temporal knowledge and propose Iterative Prototype Harmonizing (IPH) to encourage the model to learn discriminative representations in an unsupervised manner. The core principle of IPH is to maintain the harmonization of clustered prototypes across different iterations. Both AdaPL and IPH can be easily incorporated into prior pseudo labeling-based models to extract features from unlabeled medical data for accurate classification. Extensive experiments on three semi-supervised medical image datasets demonstrate that our method outperforms state-of-the-art approaches. The code is available at https://github.com/CityU-AIM-Group/TEAR.

KW - Medical image classification

KW - Prototype harmonizing

KW - Pseudo labeling

KW - Semi-supervised learning

KW - Temporal knowledge

U2 - 10.1007/978-3-031-16452-1_12

DO - 10.1007/978-3-031-16452-1_12

M3 - Article in proceedings

AN - SCOPUS:85138992658

SN - 9783031164514

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 119

EP - 129

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings

A2 - Wang, Linwei

A2 - Dou, Qi

A2 - Fletcher, P. Thomas

A2 - Speidel, Stefanie

A2 - Li, Shuo

PB - Springer

T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022

Y2 - 18 September 2022 through 22 September 2022

ER -