用自监督预训练模型提升多实例学习在脑血肿CT中的分类效果
Multi-instance Learning as Downstream Task of Self-Supervised Learning-based Pre-trained Model
- 将自监督预训练模型作为多实例学习的下游任务
- 在256实例情况下，准确率提升5%~13%，F1提高40%~55%
- 特别适合处理存在伪相关性的医学图像分类任务
在深度多实例学习中，样本数量依赖于数据集。在组织病理图像中，深度学习多实例学习通常假设每袋包含数百至数千个实例。然而，当脑血肿CT中每袋实例数增至256时，学习变得极为困难。本文针对此问题提出新方法：将自监督学习预训练模型作为多实例学习的下游任务。即使原始目标任务存在伪相关性，该方法在脑血肿CT低密度标记分类中仍实现准确率提升5%~13%，F1值提升40%~55%。
原文摘要 · Abstract (English)
In deep multi-instance learning, the number of applicable instances depends on the data set. In histopathology images, deep learning multi-instance learners usually assume there are hundreds to thousands instances in a bag. However, when the number of instances in a bag increases to 256 in brain hematoma CT, learning becomes extremely difficult. In this paper, we address this drawback. To overcome this problem, we propose using a pre-trained model with self-supervised learning for the multi-instance learner as a downstream task. With this method, even when the original target task suffers from the spurious correlation problem, we show improvements of 5% to 13% in accuracy and 40% to 55% in the F1 measure for the hypodensity marker classification of brain hematoma CT.
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