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Clustering-enhanced active learning with dynamic sampling for brain tumor classification
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AI
DOI：10.1016/j.bspc.2026.109715.png)
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Abstract
En 中文
Automated classification of brain tumors is essential for reliable diagnosis and effective treatment planning. However, deep learning-based methods require large, well-labeled MRI datasets, which can be expensive, time-consuming, and challenging to obtain in clinical settings. Moreover, real-world datasets often exhibit severe class imbalance and inter-subject variability, both of which can compromise model robustness and limit generalization to unseen cases. In this paper, we introduce a novel dynamic active learning framework enhanced by clustering for brain tumor classification. First, the proposed framework extracts high-level features of MRI images by a self-supervised learning method, which are then clustered to form a multi-class data pool, providing a pre-classification of the samples. To reduce annotation effort while maintaining model performance, the framework dynamically selects the most informative samples from each cluster by jointly considering prediction uncertainty and cluster diversity. Additionally, we have constructed a high-quality brain tumor MRI dataset that includes three tumor types: glioma, metastatic tumor, and diffuse large B-cell lymphoma. Notably, the latter is scarce in existing public datasets. Extensive experiments on both public and private datasets show that the proposed method achieves competitive performance using only a small portion of labeled data. Also, on an external test set, the method obtained an average accuracy of 0.92. All these results suggest that our method offers a practical and efficient solution for MRI-based brain tumor classification in real-world clinical settings.
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