Adaptive sample selection with joint loss for medical image classification under label noise

Tongqing Xue; Aiping Qu; Han Hong

doi:10.1117/12.3045588

15 January 2025 Adaptive sample selection with joint loss for medical image classification under label noise

Tongqing Xue, Aiping Qu, Han Hong

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Proceedings Volume 13513, The International Conference Optoelectronic Information and Optical Engineering (OIOE2024); 135131W (2025) https://doi.org/10.1117/12.3045588
Event: The International Conference Optoelectronic Information and Optical Engineering (OIOE2024), 2024, Wuhan, China

Abstract

Deep Neural Networks (DNNs) have been extensively employed in the classification of medical images and achieve impressive performance. But the success of DNNs is dependent on the large amounts of correctly labelled images. However, noisy labels are often unavoidable in the real-world clinical scenarios, which significantly impact the performance of the model. In this manuscript, we introduce a new sample selection method which could select the clean samples adaptively without knowing the prior knowledge, such as label noise rates. We also integrated semi-supervised learning during sample selection to fully utilize the noised dataset. Specifically, we calculate batch statistics in each mini-batch and divide the samples into clean and noisy based on the statistics, then they are served as labelled and unlabeled in the semi-supervised manner. Furthermore, we use a joint loss to leverage useful information from unlabeled data along with a supervised loss, which strengthens the model's robustness. To evaluate the effectiveness of our method, we conduct sufficient experiments on a medical image dataset: Chaoyang. The results show that the proposed method could deal with the noisy labels in real-world scenarios.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Tongqing Xue, Aiping Qu, and Han Hong "Adaptive sample selection with joint loss for medical image classification under label noise", Proc. SPIE 13513, The International Conference Optoelectronic Information and Optical Engineering (OIOE2024), 135131W (15 January 2025); https://doi.org/10.1117/12.3045588

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