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.

翻译：全切片图像分类是计算病理学和医学领域的一个关键挑战。基于注意力机制的多示例学习（MIL）已成为解决该问题的有效方法。然而，对于生物医学图像，注意力机制架构对模型性能的影响尚未得到充分记录。在本研究中，我们比较了包括深度学习变体在内的多种MIL方法及其实现。我们提出了一种在深度MIL中使用高维特征空间的新方法。我们还开发了一种用于全切片图像分类的新型算法，该算法将极限学习机与基于注意力的MIL相结合，以提高灵敏度并降低训练复杂度。我们将算法应用于检测外周血中循环稀有细胞（如成红细胞）的问题。我们的结果表明，非线性在分类中起着关键作用，移除非线性会导致稳定性急剧下降，同时曲线下平均面积（AUC）降低超过4%。我们还证明，利用高维特征空间可显著提升模型的鲁棒性，平均AUC提高超过10%。此外，我们展示了极限学习机在训练效率方面能带来明显改进，可将训练参数数量减少至原来的五分之一，同时将平均AUC维持在深度MIL模型的1.5%误差范围内。最后，我们探讨了未来用量子算法增强经典计算框架的可能性。因此，本研究有助于为更准确、高效的单细胞诊断（精准医学的基石之一）铺平道路。