标题： 基于注意力的全切片图像分类的极限学习机 显示英文标题

标题： Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification

摘要： 全切片图像分类是计算病理学和医学中的一个关键挑战。基于注意力的多实例学习（MIL）已成为解决此问题的有效方法。然而，注意力机制架构对模型性能的影响在生物医学图像中尚未得到充分记录。在本工作中，我们比较了不同的MIL方法和实现，包括深度学习变体。我们引入了一种新的方法，使用高维特征空间进行深度MIL。我们还开发了一种用于全切片图像分类的新算法，其中极端机器学习与基于注意力的MIL相结合，以提高灵敏度并降低训练复杂性。我们将算法应用于检测外周血中的循环稀有细胞（CRCs），如网织红细胞。我们的结果表明，非线性在分类中起着关键作用，因为去除它们会导致稳定性急剧下降，并且平均曲线下面积（AUC）减少了超过4%。我们还证明，当利用高维特征空间时，模型的鲁棒性显著提高，平均AUC提高了10%以上。此外，我们展示了极端学习机在训练效率方面可以提供明显的改进，通过将训练参数数量减少5倍，同时保持平均AUC在深度MIL模型的1.5%以内。最后，我们讨论了未来如何用量子算法丰富经典计算框架的选项。因此，这项工作可以为更准确和高效的单细胞诊断铺平道路，这是精准医学的一个基本组成部分。 显示英文摘要

摘要： Whole-slide image classification represents a key challenge in computational pathology and medicine. Attention-based multiple instance learning (MIL) has emerged as an effective approach for this problem. However, the effect of attention mechanism architecture on model performance is not well-documented for biomedical imagery. In this work, we compare different methods and implementations of MIL, including deep learning variants. We introduce a new method using higher-dimensional feature spaces for deep MIL. We also develop a novel algorithm for whole-slide image classification where extreme machine learning is combined with attention-based MIL to improve sensitivity and reduce training complexity. We apply our algorithms to the problem of detecting circulating rare cells (CRCs), such as erythroblasts, in peripheral blood. Our results indicate that nonlinearities play a key role in the classification, as removing them leads to a sharp decrease in stability in addition to a decrease in average area under the curve (AUC) of over 4%. We also demonstrate a considerable increase in robustness of the model with improvements of over 10% in average AUC when higher-dimensional feature spaces are leveraged. In addition, we show that extreme learning machines can offer clear improvements in terms of training efficiency by reducing the number of trained parameters by a factor of 5 whilst still maintaining the average AUC to within 1.5% of the deep MIL model. Finally, we discuss options of enriching the classical computing framework with quantum algorithms in the future. This work can thus help pave the way towards more accurate and efficient single-cell diagnostics, one of the building blocks of precision medicine.