Abstract
Identifying high-quality and easily accessible annotated samples poses a notable challenge in medical image analysis. Transfer learning techniques, leveraging pre-training data, offer a flexible solution to this issue. However, the impact of fine-tuning diminishes when the dataset exhibits an irregular distribution between classes. This paper introduces a novel deep convolutional neural network, named Curriculum Learning and Progressive Self-supervised Training (CURVETE). CURVETE addresses challenges related to limited samples, enhances model generalisability, and improves overall classification performance. It achieves this by employing a curriculum learning strategy based on the granularity of sample decomposition during the training of generic unlabelled samples. Moreover, CURVETE address the challenge of irregular class distribution by incorporating a class decomposition approach in the downstream task. The proposed method undergoes evaluation on three distinct medical image datasets: brain tumour, digital knee x-ray, and Mini-DDSM datasets. We investigate the classification performance using a generic self-supervised sample decomposition approach with and without the curriculum learning component in training the pretext task. Experimental results demonstrate that the CURVETE model achieves superior performance on test sets with an accuracy of 96.60% on the brain tumour dataset, 75.60% on the digital knee x-ray dataset, and 93.35% on the Mini-DDSM dataset using the baseline ResNet-50. Furthermore, with the baseline DenseNet-121, it achieved accuracies of 95.77%, 80.36%, and 93.22% on the brain tumour, digital knee x-ray, and Mini-DDSM datasets, respectively, outperforming other training strategies.
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URL
https://arxiv.org/abs/2510.23442
